CN107886960B - A kind of audio signal reconstruction method and device - Google Patents

Abstract

Embodiments of the present invention disclose an audio signal reconstruction method and device. The method includes: acquiring compressed data corresponding to at least two audio signals; and performing inverse quantization on the compressed data corresponding to at least two audio signals, thereby obtaining at least two audio signals. The measurement data corresponding to the audio signals; obtaining measurement matrices, and jointly reconstructing the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrices corresponding to the at least two audio signals; Sparse inverse transform to obtain at least two audio signals. By adopting the embodiments of the present invention, the quality of the audio signal can be improved.

Description

A kind of audio signal reconstruction method and device

technical field

The present invention relates to the field of communication technologies, and in particular, to an audio signal reconstruction method and device.

Background technique

With the development of communication technology, the demand for high-quality audio-visual experience is increasing day by day, and more and more scenes need to be reconstructed with high-quality sound field information, such as: remote conferences, movies and large-scale online games. In recent years, Compressed Sensing (CS) theory fully considers the sparse characteristics of signals, and uses the structural features of signals to compress and reconstruct signals, which has become a research hotspot in the field of signal processing. The CS theory points out that since the fundamental goal of signal compression is to remove the redundant components contained in it, the compressed representation (ie, compressed data) can be directly obtained for a signal that has redundancy in itself, omitting the sampling of a large number of useless signals. Therefore, when the CS theory acts on the audio signal, it can realize the combination of the sampling and compression process of the audio signal, which greatly simplifies the entire compression process, and breaks through the bottleneck of Shannon's sampling theorem in a sense, making high-resolution sampling and compression. The acquisition of audio signals becomes possible.

The traditional audio signal reconstruction method is specifically as follows: obtaining compressed data corresponding to the audio signal; performing inverse quantization on the compressed data corresponding to the audio signal to obtain measurement data corresponding to the audio signal; signal is reconstructed. The measurement data corresponding to the audio signal obtained by inverse quantization is a system of underdetermined equations. Since there are at least two sets of solutions in the system of underdetermined equations, the reconstructed audio signal is any one of the above at least two sets of solutions, then the reconstructed The similarity between the audio signal and the collected original audio signal is low, resulting in poor quality of the reconstructed audio signal.

SUMMARY OF THE INVENTION

The present application provides an audio signal reconstruction method and device, which can improve the quality of the audio signal.

A first aspect provides an audio signal reconstruction method, the method includes: acquiring compressed data corresponding to at least two audio signals; inverse quantizing the compressed data corresponding to the at least two audio signals, so as to obtain at least two audio signals corresponding to obtain the measurement matrix, and jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrix corresponding to the at least two audio signals; perform sparse inverse transformation on the sparse transformation coefficients corresponding to the at least two audio signals , get at least two audio signals.

In a specific implementation, after obtaining the compressed data corresponding to the at least two audio signals, the terminal may perform inverse quantization on the compressed data corresponding to the at least two audio signals, thereby obtaining measurement data corresponding to the at least two audio signals, and the terminal may also obtain the measurement data. matrix, according to the measurement data corresponding to the at least two audio signals and the measurement matrix, jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals, and perform sparse inverse transformation on the sparse transformation coefficients corresponding to the at least two audio signals to obtain at least two Audio signal, which can improve the quality of at least two audio signals.

In the above technical solution, optionally, the at least two audio signals may include a first audio signal and a second audio signal, and the terminal jointly reconstructs the at least two audio signals according to the measurement data and measurement matrix corresponding to the at least two audio signals The corresponding sparse transformation coefficients, specifically:

The sparse transformation coefficient corresponding to the second audio signal is calculated according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix.

In the above technical solution, optionally, the first audio signal may correspond to the first channel, the second audio signal may correspond to the second channel, and the first audio signal and the second audio signal are audio signals obtained by collecting in the same time period, Then, the terminal calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, which may be specifically:

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, determine the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second amplitude as the sparse transformation coefficient corresponding to the second audio signal. Amplitude prior, according to the measurement data corresponding to the second audio signal and the measurement matrix, calculate the amplitude in the sparse transform coefficient corresponding to the second audio signal, wherein the ratio of the first amplitude to the second amplitude is the microphone corresponding to the first audio signal The ratio of the logarithm of the distance from the sound source to the logarithm of the distance of the microphone corresponding to the second audio signal from the sound source.

In the above technical solution, optionally, the first audio signal may correspond to the first channel, the second audio signal may correspond to the second channel, and the first audio signal and the second audio signal are audio signals obtained by collecting in the same time period, Then, the terminal calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, which may be specifically:

According to the first phase in the sparse transformation coefficient corresponding to the first audio signal, determine the second phase in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second phase as the second phase in the sparse transformation coefficient corresponding to the second audio signal The prior of the phase, according to the measurement data corresponding to the second audio signal and the measurement matrix, calculate the phase in the sparse transformation coefficient corresponding to the second audio signal, wherein the ratio of the first phase to the second phase is the microphone corresponding to the first audio signal The ratio of the distance from the sound source to the distance from the microphone corresponding to the second audio signal from the sound source.

In the above technical solution, optionally, the first audio signal may correspond to the first channel, the second audio signal may correspond to the second channel, and the first audio signal and the second audio signal are audio signals obtained by collecting in the same time period, Then, the terminal calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, which may be specifically:

Determine the first frequency in the sparse transformation coefficient corresponding to the first audio signal as the second frequency in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second frequency as the sparse transformation coefficient corresponding to the second audio signal The prior of the medium frequency is to calculate the frequency in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

In the above technical solution, optionally, the first audio signal and the second audio signal may correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods. The sparse transformation coefficient corresponding to the signal, the measurement data and the measurement matrix corresponding to the second audio signal, and the sparse transformation coefficient corresponding to the second audio signal is calculated, which may be specifically:

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, determine the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second amplitude as the sparse transformation coefficient corresponding to the second audio signal. A priori of the amplitude, according to the measurement data corresponding to the second audio signal and the measurement matrix, calculate the amplitude in the sparse transformation coefficient corresponding to the second audio signal, wherein the coefficient transformation coefficients corresponding to the audio signals in different time periods of the same channel are The amplitude has a linear relationship with the sequence numbers of the frames corresponding to the audio signals of different time periods.

In the above technical solution, optionally, the first audio signal and the second audio signal may correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods. The sparse transformation coefficient corresponding to the signal, the measurement data and the measurement matrix corresponding to the second audio signal, and the sparse transformation coefficient corresponding to the second audio signal is calculated, which may be specifically:

Determining the first phase in the sparse transformation coefficient corresponding to the first audio signal as the second phase in the prior sparse transformation coefficient corresponding to the second audio signal, and using the second phase as the second phase in the sparse transformation coefficient corresponding to the second audio signal A priori of the phase, and according to the measurement data corresponding to the second audio signal and the measurement matrix, the phase in the sparse transformation coefficient corresponding to the second audio signal is calculated.

In the above technical solution, optionally, the first audio signal and the second audio signal may correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods. The sparse transformation coefficient corresponding to the signal, the measurement data and the measurement matrix corresponding to the second audio signal, and the sparse transformation coefficient corresponding to the second audio signal is calculated, which may be specifically:

According to the first frequency in the sparse transformation coefficient corresponding to the first audio signal, determine the second frequency in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second frequency as the sparse transformation coefficient corresponding to the second audio signal. The prior of the frequency, according to the measurement data corresponding to the second audio signal and the measurement matrix, calculate the frequency in the sparse transformation coefficient corresponding to the second audio signal, wherein the first frequency and the second frequency have an intersection, and the frequency in the intersection is determined by comparing the first frequency and the second frequency. The frequencies in the frequency are randomly selected.

In the above technical solution, optionally, the first audio signal and the second audio signal are audio signals collected in adjacent time periods.

In the above technical solution, optionally, the at least two audio signals may include a third audio signal, and the terminal jointly reconstructs the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrix corresponding to the at least two audio signals , which can be specifically:

The sparse transformation coefficient corresponding to the third audio signal is calculated according to the preset initial sparse transformation coefficient, the measurement data corresponding to the third audio signal, and the measurement matrix.

A second aspect provides a computer storage medium, where the computer storage medium stores a program, and when the program is executed, it includes all or part of the steps in the audio signal reconstruction method provided in the first aspect of the embodiments of the present application.

A third aspect provides an audio signal reconstruction apparatus, the apparatus may include a compressed data acquisition module, an inverse quantization module, a joint reconstruction module and an inverse sparse transform module, the apparatus may be used to implement part or all of the combination of the first aspect step.

A fourth aspect provides a terminal including a processor and a memory, where the processor can be used to implement some or all of the steps in combination with the first aspect.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without creative effort.

1A is a schematic interface diagram of a microphone array provided in an embodiment of the present invention;

1B is a schematic diagram of an interface for transferring parameter information provided in an embodiment of the present invention;

1C is a schematic diagram of an interface for reconstructing an audio signal provided in an embodiment of the present invention;

1D is a schematic interface diagram of a sampled audio signal provided in an embodiment of the present invention;

1E is a schematic flowchart of a reconstructed audio signal provided in an embodiment of the present invention;

2 is a schematic flowchart of an audio signal reconstruction method provided in an embodiment of the present invention;

3 is a schematic flowchart of an audio signal reconstruction method provided in another embodiment of the present invention;

4 is a schematic flowchart of an audio signal compression method provided in an embodiment of the present invention;

5 is a schematic structural diagram of an audio signal reconstruction apparatus provided in an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of a terminal provided in an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly described below with reference to the accompanying drawings in the embodiments of the present invention.

An embodiment of the present invention provides an audio signal reconstruction method. A terminal can obtain compressed data corresponding to at least two audio signals, and perform inverse quantization on the compressed data corresponding to the at least two audio signals, thereby obtaining measurements corresponding to the at least two audio signals. data, and obtain a measurement matrix, jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrices corresponding to the at least two audio signals, and perform sparse inverse transformation on the sparse transformation coefficients corresponding to the at least two audio signals, Obtaining at least two audio signals can improve the quality of the audio signals.

In the specific implementation, the terminal can collect audio signals from the sound source in parallel through an array composed of multiple microphones. The audio signal collected by any microphone can be used as the audio signal of one channel, and the audio signals of different channels are collected by different microphones. of. The array composed of multiple microphones may be a linear type, a ring type, or a star type, etc., which is not specifically limited by the embodiment of the present invention. Taking the interface schematic diagram of the microphone array shown in FIG. 1A as an example, each microphone (such as Mic_1, Mic_2, Mic_3...Mic_N) can be arranged on the right side of the sound source, the array composed of multiple microphones can be linear, and the distance between each microphone is the same, the audio signal collected by each microphone can be expressed as x(t) =ccos(2πf+θ), where the audio signal collected by each microphone has spatial correlation, and the audio signal collected by any microphone has correlation in time domain and frequency domain, then the terminal can sample each channel The audio signal of each channel is sparsely transformed (by FFT or MDCT, etc.), perceptual measurement (multiplying the sparse transformation coefficient obtained by the sparse transformation with the random measurement matrix), and the perceptual measurement obtained by the perceptual measurement. The noise vectors corresponding to the acquisition environment of the audio signal are added and processed to obtain the compressed data of each channel.

When the audio signal needs to be reconstructed, the terminal can obtain the compressed data corresponding to at least two audio signals, perform inverse quantization on the compressed data corresponding to the at least two audio signals, so as to obtain the measurement data corresponding to the at least two audio signals, and obtain the compressed data corresponding to the at least two audio signals. The measurement matrix, according to the measurement data corresponding to the at least two audio signals and the measurement matrix, jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals, and perform sparse inverse transformation on the sparse transformation coefficients corresponding to the at least two audio signals to obtain at least two audio signal.

It should be noted that, in this embodiment of the present invention, the terminal that compresses and samples the audio signal and the terminal that reconstructs the audio signal may be the same terminal. For example, after the terminal samples the audio signal of each channel, the audio signal of each channel is sparsely The compressed data is obtained by processing such as transformation and perceptual measurement. When the terminal needs to reconstruct the above-mentioned audio signal, it can perform inverse quantization on the compressed data corresponding to at least two audio signals, so as to obtain the measurement data corresponding to at least two audio signals. The measurement data and the measurement matrix corresponding to the two audio signals are jointly reconstructed with the sparse transformation coefficients corresponding to the at least two audio signals, and the sparse transformation coefficients corresponding to the at least two audio signals are subjected to sparse inverse transformation to obtain at least two audio signals. Optionally, in this embodiment of the present invention, the terminal that compresses and samples the audio signal and the terminal that reconstructs the audio signal may be different terminals. The compressed data is obtained by processing such as sparse transformation and perceptual measurement. The first terminal can send the compressed data to the second terminal. When the second terminal needs to reconstruct the above audio signal, it can predict the parameter information of the channel located in the frame, according to The compressed sampling data of the channel located in the frame and its parameter information are reconstructed from the audio signal of the channel located in the frame.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of an audio signal reconstruction method provided in an embodiment of the present invention. As shown in the figure, the audio signal reconstruction method in the embodiment of the present invention may at least include:

S201. Acquire compressed data corresponding to at least two audio signals.

The terminal may acquire compressed data corresponding to at least two audio signals. In a specific implementation, the terminal may obtain compressed data corresponding to at least two audio signals stored in the memory of the terminal. For example, after the terminal collects the audio signals, it compresses and samples the collected audio signals to obtain compressed data, and stores the compressed data. For another example, after another terminal collects the audio signal, the other terminal compresses and samples the collected audio signal to obtain compressed data, and then the other terminal sends the compressed data to the terminal, and the terminal stores the compressed data in the memory.

S202. Perform inverse quantization on the compressed data corresponding to the at least two audio signals, thereby obtaining measurement data corresponding to the at least two audio signals.

S203. Obtain a measurement matrix, and jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrix corresponding to the at least two audio signals.

The terminal may acquire the measurement matrix, and jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals according to the measurement data and the measurement matrix corresponding to the at least two audio signals. The measurement matrix may be a random measurement matrix.

Optionally, the at least two audio signals may include a first audio signal and a second audio signal, and the terminal may calculate the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix. The sparse transform coefficients corresponding to the audio signal. In specific implementation, the terminal may use the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix as the input of a preset reconstruction algorithm (such as AMP or GAMP, etc.), and the obtained preset reconstruction algorithm has The output may be sparse transform coefficients corresponding to the second audio signal.

Optionally, the audio signal has a spatial correlation, when the first audio signal corresponds to the first channel, the second audio signal corresponds to the second channel, and the first audio signal and the second audio signal are audio signals acquired during the same period of collection, Then the terminal can determine the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal according to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, and use the second amplitude as the sparse transformation coefficient corresponding to the second audio signal. A priori of the amplitude in the transform coefficient, according to the measurement data corresponding to the second audio signal and the measurement matrix, to calculate the amplitude in the sparse transform coefficient corresponding to the second audio signal. The ratio of the first amplitude to the second amplitude is the ratio of the logarithm of the distance between the microphone corresponding to the first audio signal and the sound source and the logarithm of the distance between the microphone corresponding to the second audio signal and the sound source.

Optionally, the audio signal has spatial correlation, when the first audio signal corresponds to the first channel, the second audio signal corresponds to the second channel, and the first audio signal and the second audio signal are audio signals obtained by collecting in the same period of time. , the terminal can determine the second phase in the prior sparse transform coefficient corresponding to the second audio signal according to the first phase in the sparse transform coefficient corresponding to the first audio signal, and use the second phase as the sparse transform corresponding to the second audio signal A priori of the phase in the coefficient, and according to the measurement data corresponding to the second audio signal and the measurement matrix, the phase in the sparse transformation coefficient corresponding to the second audio signal is calculated. The ratio of the first phase to the second phase is the ratio of the distance between the microphone corresponding to the first audio signal and the sound source and the distance between the microphone corresponding to the second audio signal and the sound source.

Optionally, the audio signal has spatial correlation, when the first audio signal corresponds to the first channel, the second audio signal corresponds to the second channel, and the first audio signal and the second audio signal are audio signals obtained by collecting in the same period of time. , the terminal may determine the first frequency in the sparse transformation coefficient corresponding to the first audio signal as the second frequency in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second frequency as the sparse transformation coefficient corresponding to the second audio signal A priori of the frequency in the transform coefficient, and according to the measurement data corresponding to the second audio signal and the measurement matrix, the frequency in the sparse transform coefficient corresponding to the second audio signal is calculated.

Optionally, the audio signal has a correlation in the time domain. When the first audio signal and the second audio signal correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods, the terminal can According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, determine the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second amplitude as the amplitude in the sparse transformation coefficient corresponding to the second audio signal , and according to the measurement data corresponding to the second audio signal and the measurement matrix, the amplitude in the sparse transform coefficient corresponding to the second audio signal is calculated. Wherein, the amplitudes in the coefficient transform coefficients corresponding to the audio signals of the same channel in different time periods have a linear relationship with the serial numbers of the frames corresponding to the audio signals of different time periods.

Optionally, the audio signal has a correlation in the time domain. When the first audio signal and the second audio signal correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods, the terminal can Determining the first phase in the sparse transformation coefficient corresponding to the first audio signal as the second phase in the prior sparse transformation coefficient corresponding to the second audio signal, and using the second phase as the second phase in the sparse transformation coefficient corresponding to the second audio signal A priori of the phase, and according to the measurement data corresponding to the second audio signal and the measurement matrix, the phase in the sparse transformation coefficient corresponding to the second audio signal is calculated.

Optionally, the audio signal has a correlation in the time domain. When the first audio signal and the second audio signal correspond to the same channel, and the first audio signal and the second audio signal are audio signals collected in different time periods, the terminal can According to the first frequency in the sparse transformation coefficient corresponding to the first audio signal, determine the second frequency in the prior sparse transformation coefficient corresponding to the second audio signal, and use the second frequency as the frequency in the sparse transformation coefficient corresponding to the second audio signal and the frequency in the sparse transform coefficient corresponding to the second audio signal is calculated according to the measurement data corresponding to the second audio signal and the measurement matrix. Wherein, there is an intersection between the first frequency and the second frequency, and the frequency in the intersection is obtained by randomly selecting the frequencies in the first frequency.

Optionally, when the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal may be audio signals collected in adjacent time periods. Exemplarily, the first audio signal may be an audio signal acquired during a first period of time, the second audio signal may be an audio signal acquired during a second period of time, and the first period of time may be earlier than the second period of time.

Optionally, the at least two audio signals may include a third audio signal, and the terminal may calculate the sparse transformation coefficient corresponding to the third audio signal according to a preset initial sparse transformation coefficient, measurement data corresponding to the third audio signal, and a measurement matrix.

Taking the schematic flowchart of reconstructing an audio signal shown in FIG. 1E as an example, the terminal can obtain compressed sampling data with different channels located in different frames, and perform inverse quantization on the audio signals with different channels located in different frames to obtain audio with different channels located in different frames. The measurement data corresponding to the signal, when the designated channel is the first channel (for example, i=1) and the designated frame is the starting frame, the terminal can initialize to obtain the initial sparse transformation coefficients. When the transmission direction of the sparse transformation coefficients is the forward transmission direction ( For example, FB=1), the terminal can calculate the sparse transform coefficient corresponding to the audio signal with the first channel located in the starting frame according to the initialized sparse transform coefficient, the measurement data corresponding to the audio signal with the first channel located in the starting frame, and the measurement matrix.

The terminal may also determine a priori sparse transformation coefficients corresponding to the audio signal of the second channel (for example, i++) located in the starting frame according to the sparse transform coefficient corresponding to the audio signal of the first channel located in the starting frame, and set the second channel located in the starting frame. The a priori sparse transform coefficient corresponding to the audio signal of the first frame is taken as the prior of the sparse transform coefficient corresponding to the audio signal of the second channel located in the starting frame, according to the measurement data and the measurement matrix corresponding to the audio signal of the second channel located in the starting frame , calculate the sparse transform coefficients corresponding to the audio signal with the second channel located in the starting frame, until the sparse transform coefficients corresponding to the audio signal with the last channel (eg i=k) located in the starting frame are obtained.

After the terminal obtains the sparse transform coefficient corresponding to the audio signal whose last channel is located in the starting frame, it can update the transfer direction of the sparse transform coefficient to the reverse transfer direction (for example, FB≠1), and then according to the audio signal of the last channel located in the starting frame The sparse transform coefficient corresponding to the signal, determine the a priori sparse transform coefficient corresponding to the audio signal whose last channel (for example, i--) is located in the starting frame, and set the previous channel of the last channel at the starting frame. The prior sparse transform coefficient corresponding to the audio signal is taken as the prior of the sparse transform coefficient corresponding to the audio signal whose last channel is located in the starting frame. The measurement data and the measurement matrix are used to calculate the sparse transformation coefficient corresponding to the audio signal whose last channel is located in the starting frame until the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the starting frame is obtained.

After the terminal obtains the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the starting frame, it can also determine the audio signal whose first channel is located in the second frame according to the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the starting frame. The prior sparse transform coefficients corresponding to the signal, and the prior sparse transform coefficients corresponding to the audio signals of the first channel located in the second frame are used as the prior of the sparse transform coefficients corresponding to the audio signals of the first channel located in the second frame. For the measurement data and the measurement matrix corresponding to the audio signal of a channel located in the second frame, the sparse transformation coefficient corresponding to the audio signal of the first channel located in the second frame is calculated.

After the terminal calculates and obtains the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the second frame, it can also determine that the second channel (for example, i++) is located in the second channel according to the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the second frame. The prior sparse transform coefficient corresponding to the audio signal of the frame, and the prior sparse transform coefficient corresponding to the audio signal of the second channel located in the second frame as the prior of the sparse transform coefficient corresponding to the audio signal of the second channel located in the second frame , according to the measurement data and the measurement matrix corresponding to the audio signal of the second channel located in the second frame, calculate the sparse transformation coefficient corresponding to the audio signal of the second channel located in the second frame, until the last channel (for example, i=k) is located in the first The sparse transform coefficients corresponding to the audio signals of two frames.

After obtaining the sparse transform coefficient corresponding to the audio signal whose last channel is located in the second frame, the terminal can update the transfer direction of the sparse transform coefficient to the reverse transfer direction (for example, FB≠1), and then according to the audio signal of the last channel located in the second frame The sparse transformation coefficient corresponding to the signal, determine the prior sparse transformation coefficient corresponding to the audio signal whose last channel (for example, i--) is located in the second frame, and set the last channel of the last channel in the second frame. The prior sparse transform coefficient corresponding to the audio signal is taken as the prior of the sparse transform coefficient corresponding to the audio signal whose last channel is located in the second frame. The measurement data and the measurement matrix are used to calculate the sparse transformation coefficient corresponding to the audio signal whose last channel is located in the second frame until the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the second frame is obtained.

After obtaining the sparse transformation coefficient corresponding to the audio signal of the first channel located in the second frame, the terminal may also determine the audio of the first channel located in the third frame according to the sparse transformation coefficient corresponding to the audio signal of the first channel located in the second frame The prior sparse transform coefficients corresponding to the signals, and the prior sparse transform coefficients corresponding to the audio signals of the first channel located in the third frame are used as the prior of the sparse transform coefficients corresponding to the audio signals of the first channel located in the third frame. For the measurement data and the measurement matrix corresponding to the audio signal of a channel located in the third frame, the sparse transformation coefficient corresponding to the audio signal of the first channel located in the third frame is calculated.

Similarly, the terminal can obtain the sparse transform coefficient corresponding to the audio signal whose first channel is located in the last frame by calculating in the above manner. Assuming that the last frame is the t-th frame, further, the terminal may determine the prior sparseness corresponding to the audio signal whose first channel is located at the t-1th frame according to the sparse transformation coefficient corresponding to the audio signal whose first channel is located at the t-th frame transform coefficients, and use the prior sparse transform coefficients corresponding to the audio signal whose first channel is located at the t-1th frame as the prior of the sparse transform coefficients corresponding to the audio signal whose first channel is located at the t-1th frame. The measurement data and the measurement matrix corresponding to the audio signal located in the t-1th frame are used to calculate the sparse transformation coefficient corresponding to the audio signal of the first channel located in the t-1th frame. Further, the terminal may determine the prior sparse transformation coefficient corresponding to the audio signal whose first channel is located in the t-2th frame according to the sparse transformation coefficient corresponding to the audio signal whose first channel is located in the t-1th frame, and convert the first channel into the t-2th frame. The prior sparse transform coefficients corresponding to the audio signal whose channel is located in the t-2th frame is taken as the prior of the sparse transform coefficients corresponding to the audio signal whose first channel is located in the t-2th frame. The measurement data and measurement matrix corresponding to the audio signal are used to calculate the sparse transformation coefficients corresponding to the audio signals whose first channel is located in the t-2th frame, until the sparse transformation coefficients corresponding to the audio signals whose first channel is located in the starting frame are obtained.

S204. Perform sparse inverse transform on the sparse transform coefficients corresponding to the at least two audio signals to obtain at least two audio signals.

The terminal may perform sparse inverse transform on the sparse transform coefficients corresponding to the at least two audio signals to obtain at least two audio signals. Exemplarily, the terminal may perform inverse time-frequency transform on the sparse transform coefficients corresponding to the at least two audio signals by means of IMDCT or IFFT, to obtain at least two audio signals.

In the audio signal reconstruction method shown in FIG. 2 , the terminal may obtain compressed data corresponding to at least two audio signals, and perform inverse quantization on the compressed data corresponding to at least two audio signals, thereby obtaining measurement data corresponding to at least two audio signals , and obtain the measurement matrix, according to the measurement data and the measurement matrix corresponding to the at least two audio signals, jointly reconstruct the sparse transformation coefficients corresponding to the at least two audio signals, and perform sparse inverse transformation on the sparse transformation coefficients corresponding to the at least two audio signals to obtain At least two audio signals to improve the quality of the audio signal.

Referring to FIG. 3, FIG. 3 is a schematic flowchart of an audio signal reconstruction method provided in an embodiment of the present invention. As shown in the figure, the audio signal reconstruction method in the embodiment of the present invention may at least include:

S301, the first terminal samples the collected audio signals of each channel, and the sampled audio signals of each channel are collected in the same time period.

The first terminal may collect audio signals through each microphone, wherein the frequencies of the audio signals collected by the microphones are the same, that is, the support sets of the audio signals collected by the microphones are the same, and the support set may include at least one support, which supports the frequency used to indicate the audio signal. Whether the frequency is the same as the specified frequency. For example, the frequency of the audio signal collected by each microphone includes 5HZ, 7HZ and 10HZ, then the support set of the audio signal collected by each microphone is 0000101001, and the first "0" in the support set indicates the frequency of the audio signal Not 1HZ, the second "0" in the support set indicates that the frequency of the audio signal is not 2HZ, the third "0" in the support set indicates that the frequency of the audio signal is not 3HZ, and the fourth "0" in the support set "Indicates that the frequency of the audio signal is not 4HZ, the first "1" in the support set indicates that the frequency of the audio signal is 5HZ, and the fifth "0" in the support set indicates that the frequency of the audio signal is not 6HZ, and the support set The second "1" in the support set indicates that the frequency of the audio signal is 7HZ, the sixth "0" in the support set indicates that the frequency of the audio signal is not 8HZ, and the seventh "0" in the support set indicates the frequency of the audio signal Instead of 9HZ, the third "1" in the support set indicates that the frequency of the audio signal is 10HZ.

Among them, the collected audio signal has a correlation in the frequency domain. Exemplarily, the audio signals of each channel can be represented as follows:

x(t)=ccos(2πf+θ)

where t represents the frame of the audio signal of the channel, x(t) represents the audio signal of the channel located in the t frame, c represents the amplitude of the audio signal of the channel located in the t frame, and f represents the frequency of the audio signal of the channel located in the t frame , θ denotes the phase of the channel's audio signal at frame t. The first terminal may determine that the audio signal of any channel is composed of a limited number of frequency components, and the frequencies of the audio signals located in the same channel collected in different time periods are sparse.

Among them, the collected audio signal has spatial correlation. Exemplarily, the amplitudes in the sparse transform coefficients corresponding to the audio signals of each channel collected in the same time period and the collection distance of the audio signals of the channel show logarithmic attenuation, and the audio signals of each channel collected in the same time period correspond to The phase in the sparse transform coefficient varies linearly with the collection distance of the audio signal of the channel, and the supports in the sparse transform coefficient corresponding to the audio signals of each channel collected in the same period are the same.

Among them, the collected audio signal has a correlation in the time domain. Exemplarily, the amplitudes of the audio signals of the same channel collected in different time periods are linearly correlated, the audio signals of the same channel collected in different time periods have the same phase, and the support of the audio signals of the same channel collected in different time periods is a Bernoulli distribution. .

The first terminal may use the audio signal collected by any microphone as the audio signal of one channel, and sample the collected audio signals of each channel, and obtain the audio signals of each channel collected in the same period by sampling, for example, the first terminal Sampling to obtain the audio signal of each channel located in the starting frame, or sampling to obtain the audio signal of each channel located in the second frame, or sampling to obtain the audio signal of each channel located in the last frame, and so on.

S302, the first terminal performs sparse transformation on the sampled audio signals of each channel to obtain sparse transformation coefficients.

The first terminal may perform sparse transformation on the sampled audio signals of each channel to obtain sparse transformation coefficients, that is, perform time-frequency transformation on the sampled audio signals of each channel collected in the same time period. For example, the first terminal may use an FFT algorithm or The MDCT algorithm performs sparse transformation on the audio signals of each channel collected in the same period of sampling, and obtains sparse transformation coefficients.

S303: The first terminal multiplies the preset measurement matrix by the sparse transformation coefficient to obtain a perceptual measurement value.

After obtaining the sparse transformation coefficients of the audio signals of each channel collected in the same period, the first terminal may multiply the preset measurement matrix by the sparse variable coefficients to obtain the perceptual measurement value. Wherein, the number of rows of the preset measurement matrix is smaller than the number of columns, so that sub-Nyquist sampling can be implemented, so that in the process of reconstructing the audio signal, the audio signal can be restored without distortion.

S304, the first terminal adds the perceptual measurement matrix and the noise vector to obtain compressed data.

After obtaining the sensing measurement value, the first terminal may perform matrix quantization on the sensing measurement value to obtain compressed sampling data. For example, the first terminal may acquire the noise vector corresponding to the collection environment of the audio signal of each channel, and add the perceptual measurement matrix and the noise vector to obtain compressed data.

Exemplarily, the first terminal processes audio signals whose channels are located in the same frame to obtain compressed data, which can be expressed by the following formula:

表示稀疏变换系数，W表示各个通道的音频信号的采集环境对应的噪声矢量，其中，

X_n×N表示N个通道m个帧的音频信号，m＜n。Among them, Y _m×N represents the compressed sampling data of m frames of N channels, A _m×n represents the preset measurement matrix,

represents the sparse transformation coefficient, W represents the noise vector corresponding to the acquisition environment of the audio signal of each channel, where,

X _n×N represents the audio signal of N channels and m frames, and m<n.

In a specific implementation, the first terminal may sample the audio signals whose channels are located in the starting frame, perform sparse transformation on the sampled audio signals whose channels are located in the starting frame, obtain sparse transformation coefficients, and combine the preset measurement matrix with the sparse audio signal. The transformation coefficients are multiplied to obtain the perceptual measurement value, and the perceptual measurement value is added to the noise vector to obtain the compressed data of each channel at the starting frame. Further, the first terminal can sample the audio signals whose channels are located in the second frame, perform sparse transformation on the sampled audio signals whose channels are located in the second frame, obtain sparse transformation coefficients, and combine the preset measurement matrix with the sparse transformation. The coefficients are multiplied to obtain the perceptual measurement value, and the perceptual measurement value is added to the noise vector to obtain the compressed data of each channel in the second frame. Further, the first terminal may sample the audio signals whose channels are located in the third frame, perform sparse transformation on the sampled audio signals whose channels are located in the third frame, obtain sparse transformation coefficients, and combine the preset measurement matrix with the sparse transformation. The coefficients are multiplied to obtain the perceptual measurement value, and the perceptual measurement value is added to the noise vector to obtain the compressed data of each channel in the third frame until the compressed data of each channel in the last frame is obtained.

S305, the first terminal sends the compressed data to the second terminal.

After acquiring the compressed data of each channel, the first terminal may send the compressed sampling data of each channel to the second terminal. Optionally, after acquiring the compressed sampling data of each channel located in a different frame, the first terminal may send the compressed sampling data of each channel located in a different frame to the second terminal.

S306, the second terminal determines a priori sparse transformation coefficients corresponding to the audio signal of the next channel of the channel corresponding to the audio signal of the specified frame according to the sparse transformation coefficient of one channel corresponding to the audio signal located in the specified frame.

S307, the second terminal obtains the a priori sparse transformation coefficient corresponding to the audio signal whose next channel of the channel is located in the specified frame, the measurement data and the measurement matrix corresponding to the audio signal whose next channel of the channel is located in the specified frame, and the measurement matrix of the channel. The sparse transform coefficients corresponding to the audio signal whose next channel is at the specified frame.

Optionally, the second terminal may use the a priori sparse transform coefficient corresponding to the audio signal whose next channel of the channel is located in the specified frame, the measurement data corresponding to the audio signal whose next channel of the channel is located in the specified frame, and the measurement matrix as the shell. The input of the Yess algorithm is to obtain the sparse transform coefficient corresponding to the audio signal whose next channel of this channel is located in the specified frame.

Exemplarily, the Bayesian algorithm can be expressed as follows:

表示基于x^(t)得到

的概率，

表示第m通道的位于第t帧的音频信号对应的测量数据，x^(t)表示第t帧的音频信号对应的稀疏变换系数；

表示基于

以及s_n得到

的概率，

表示第n通道的位于第t帧的音频信号对应的稀疏变换系数中的相位，

表示第n通道的位于第t帧的音频信号对应的稀疏变换系数中的幅度，s_n表示位于第n通道的音频信号对应的稀疏变换系数中的频率；

表示基于

得到

的概率，

表示第n通道的位于第t-1帧的音频信号对应的稀疏变换系数中的相位；

表示基于

得到

的概率，

表示第n通道的位于第t-1帧的音频信号对应的稀疏变换系数中的幅度；p(s_n)表示第n通道的音频信号对应的稀疏变换系数中的频率的概率。Among them, p(x, θ, c, s|y) represents the probability of obtaining x, θ, c and s based on y, x represents the sparse transformation coefficient corresponding to the audio signal, and θ represents the phase in the sparse transformation coefficient corresponding to the audio signal , c represents the amplitude in the sparse transformation coefficient corresponding to the audio signal, s represents the frequency in the sparse transformation coefficient corresponding to the audio signal, y represents the measurement data corresponding to the audio signal; T represents the frame length of the audio signal of each channel, and M represents the measurement The number of rows of the matrix, N represents the number of columns of the measurement matrix;

means based on x ^(t) to get

The probability,

represents the measurement data corresponding to the audio signal of the t-th frame of the m-th channel, and x ^(t) represents the sparse transform coefficient corresponding to the audio signal of the t-th frame;

means based on

and _sn get

The probability,

represents the phase of the nth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame,

Represents the amplitude in the sparse transformation coefficient corresponding to the audio signal of the nth channel located in the tth frame, and _sn represents the frequency in the sparse transformation coefficient corresponding to the audio signal located in the nth channel;

means based on

get

The probability,

represents the phase in the sparse transform coefficient corresponding to the audio signal of the t-1th frame of the nth channel;

means based on

get

The probability,

represents the amplitude of the sparse transform coefficient corresponding to the audio signal of the t-1th channel of the nth channel; p(s _n ) represents the probability of the frequency in the sparse transform coefficient corresponding to the audio signal of the nth channel.

表示第一通道位于第t帧的音频信号对应的测量数据，

表示第m通道位于第t帧的音频信号对应的测量数据，

表示第n通道位于第t帧的音频信号对应的稀疏变换系数，

表示第k通道位于第t帧的音频信号对应的稀疏变换系数，

表示第n通道位于第t帧的音频信号对应的稀疏变换系数中的频率，

表示第k通道位于第t帧的音频信号对应的稀疏变换系数中的频率，

表示第n通道位于第t帧的音频信号对应的稀疏变换系数中的相位，

表示第k通道位于第t帧的音频信号对应的稀疏变换系数中的相位，

表示第n通道的位于第t帧的音频信号对应的稀疏变换系数中的幅度，

表示第k通道的位于第t帧的音频信号对应的稀疏变换系数中的幅度，例如，第二终端基于

得到

第二终端可以根据

获取第n通道位于第t帧的音频信号对应的稀疏变换系数中的频率(即

)，第n通道位于第t帧的音频信号对应的稀疏变换系数中的相位(即

)，第n通道位于第t帧的音频信号对应的稀疏变换系数中的幅度(即

)，进而根据音频信号时域近场算法，基于第n通道位于第t帧的音频信号对应的稀疏变换系数中的频率、相位以及幅度，确定第n通道位于第t+1帧的音频信号对应的先验稀疏变换系数，并根据第n通道位于第t+1帧的音频信号对应的先验稀疏变换系数、第n通道位于第t+1帧的音频信号对应的测量数据以及测量矩阵，得到第n通道位于第t+1帧的音频信号对应的稀疏变换系数，例如第n通道位于第t+1帧的音频信号对应的稀疏变换系数中的相位(即

)等。又如，第二终端根据第n通道位于第t+1帧的音频信号对应的稀疏变换系数，，确定第n通道位于第t帧的音频信号对应的先验稀疏变换系数，并根据第n通道位于第t帧的音频信号对应的先验稀疏变换系数、第n通道位于第t帧的音频信号对应的测量数据以及测量矩阵，得到第n通道位于第t帧的音频信号对应的稀疏变换系数。需要说明的是，本发明实施例中不同时段采集到的同一通道的音频信号对应的稀疏变换系数在同一通道内传递，可提高通道的冗余性。The message transmission method may include a forward transmission method and a reverse transmission method, wherein the forward transmission method is to determine that the next channel of the channel is located in the specified frame according to the sparse transformation coefficient corresponding to the audio signal of the channel located in the specified frame. The a priori sparse transform coefficient corresponding to the audio signal of the channel, and the a priori sparse transform coefficient corresponding to the audio signal whose next channel of this channel is located in the specified frame, the measurement data corresponding to the audio signal whose next channel is located in the specified frame, and Measure the matrix to obtain the sparse transform coefficients corresponding to the audio signal whose next channel of this channel is located in the specified frame. Reverse transfer is to determine the prior sparse transformation coefficient corresponding to the audio signal of the audio signal whose previous channel is located in the specified frame according to the sparse transformation coefficient corresponding to the audio signal of a channel located in the specified frame, and according to the previous channel of the channel located in the specified frame. The prior sparse transformation coefficients corresponding to the audio signal of the frame, the measurement data and measurement matrix corresponding to the audio signal whose previous channel is located in the specified frame, and the sparse transformation coefficient corresponding to the audio signal whose previous channel is located in the specified frame are obtained . Taking the schematic diagram of the interface for transferring parameter information shown in FIG. 1B as an example,

represents the measurement data corresponding to the audio signal whose first channel is located in the t-th frame,

represents the measurement data corresponding to the audio signal of the mth channel located in the tth frame,

represents the sparse transform coefficient corresponding to the audio signal of the nth channel located in the tth frame,

represents the sparse transform coefficient corresponding to the audio signal of the kth channel located in the tth frame,

represents the frequency of the nth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame,

represents the frequency of the kth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame,

represents the phase of the nth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame,

represents the phase of the kth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame,

represents the amplitude in the sparse transform coefficient corresponding to the audio signal of the nth channel located in the tth frame,

represents the amplitude of the kth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame, for example, the second terminal is based on

get

The second terminal can be based on

Obtain the frequency of the sparse transform coefficient corresponding to the audio signal of the nth channel in the tth frame (ie

), the phase of the nth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame (ie

), the amplitude of the nth channel in the sparse transform coefficient corresponding to the audio signal of the tth frame (ie

), and then according to the time-domain near-field algorithm of the audio signal, based on the frequency, phase and amplitude of the sparse transformation coefficients corresponding to the audio signal of the t-th channel in the t-th frame, it is determined that the n-th channel is located in the t+1-th frame of the audio signal corresponding to and according to the prior sparse transformation coefficients corresponding to the audio signal of the nth channel located in the t+1th frame, the measurement data and the measurement matrix corresponding to the audio signal of the nth channel located in the t+1th frame, we get The sparse transform coefficient corresponding to the audio signal whose channel n is located at frame t+1, for example, the phase in the sparse transform coefficient corresponding to the audio signal whose channel is located at frame t+1 (ie

)Wait. For another example, the second terminal determines, according to the sparse transformation coefficients corresponding to the audio signal of the nth channel located in the t+1th frame, the prior sparse transformation coefficients corresponding to the audio signal of the nth channel located in the tth frame, and according to the nth channel The prior sparse transform coefficients corresponding to the audio signal located in the t-th frame, the measurement data and the measurement matrix corresponding to the audio signal of the n-th channel located in the t-th frame, are obtained. It should be noted that, in the embodiment of the present invention, the sparse transformation coefficients corresponding to the audio signals of the same channel collected in different time periods are transmitted in the same channel, which can improve the redundancy of the channels.

Taking the interface schematic diagram of the reconstructed audio signal shown in FIG. 1C as an example, the frame length of the collected audio signal is t, the number of channels is K, and the second terminal can preset the first channel (Chann 1) at the start frame ( Frame1) the corresponding initial sparse transformation coefficient of the audio signal, according to the initial sparse transformation coefficient corresponding to the audio signal of the first channel located in the initial frame, the measurement data and the measurement matrix corresponding to the audio signal of the first channel located in the initial frame, Calculate the sparse transform coefficients corresponding to the audio signal whose first channel is located in the starting frame. Further, the second terminal can use the sparse transform coefficient corresponding to the audio signal of the first channel in the initial frame as the input of the spatial near-field algorithm of the audio signal, and obtain the corresponding audio signal of the second channel (Chann 2) in the initial frame. The prior sparse transform coefficients of The sparse transform coefficients corresponding to the audio signal at the start frame. Further, the second terminal can use the sparse transform coefficient corresponding to the audio signal of the second channel in the initial frame as the input of the spatial near-field algorithm of the audio signal, and obtain the corresponding audio signal of the third channel (Chann 3) in the initial frame. The a priori sparse transformation coefficients of The sparse transform coefficients corresponding to the audio signal at the start frame.

Optionally, after obtaining the sparse transform coefficient corresponding to the audio signal of the Kth channel (Chann K) located in the starting frame, the second terminal may use the sparse transform coefficient corresponding to the audio signal located in the starting frame of the Kth channel as the audio signal. The input of the near-field algorithm in the signal space domain is to obtain the a priori sparse transformation coefficient corresponding to the audio signal located in the starting frame of the K-1th channel, according to the prior sparse transformation corresponding to the audio signal located in the starting frame of the K-1th channel coefficient, measurement data and measurement matrix corresponding to the audio signal of the K-1th channel located in the starting frame, calculate the sparse transformation coefficient corresponding to the audio signal of the K-1th channel located in the starting frame, until the first channel is located in the starting frame. The sparse transform coefficients corresponding to the audio signal of the first frame.

Optionally, after obtaining the sparse transform coefficient corresponding to the audio signal of the first channel located in the starting frame, the second terminal may use the sparse transform coefficient corresponding to the audio signal located in the starting frame of the first channel as the time-domain near field of the audio signal. The input of the algorithm is to obtain the a priori sparse transformation coefficient corresponding to the audio signal of the first channel located in the second frame, according to the a priori sparse transformation coefficient corresponding to the audio signal of the first channel located in the second frame, For the measurement data and measurement matrix corresponding to the audio signals of the two frames, the sparse transformation coefficients corresponding to the audio signals of the first channel located in the second frame are calculated. Further, the second terminal may use the sparse transform coefficient corresponding to the audio signal of the first channel located in the second frame as the input of the audio signal time-domain near-field algorithm, and obtain the prior corresponding to the audio signal of the first channel located in the third frame. The sparse transformation coefficient is calculated according to the prior sparse transformation coefficient corresponding to the audio signal of the first channel located in the third frame, the measurement data corresponding to the audio signal of the first channel located in the third frame, and the measurement matrix to calculate the first channel located in the third frame. The sparse transform coefficients corresponding to the audio signals of the frames are obtained until the sparse transform coefficients corresponding to the audio signals of the first channel located in the t-th frame (Frame t) are obtained.

Optionally, after obtaining the sparse transform coefficient corresponding to the audio signal located in the t-th frame of the first channel, the second terminal may use the sparse transform coefficient corresponding to the audio signal located in the t-th frame of the first channel as the audio signal. The input of the domain near-field algorithm, the a priori sparse transform coefficient corresponding to the audio signal located in the t-1th frame (Frame t-1) of the first channel is obtained, according to the t-1th frame of the first channel. The a priori sparse transformation coefficients corresponding to the audio signal of the first channel, the measurement data and the measurement matrix corresponding to the audio signal of the first channel located in the t-1th frame, and the audio signal of the first channel located in the t-1th frame is calculated. The corresponding sparse transform coefficients are obtained until the sparse transform coefficients corresponding to the audio signal located in the first frame of the first channel are obtained.

The spatial-domain near-field algorithm of the audio signal may specifically include: a logarithmic attenuation between the amplitudes in the sparse transform coefficients corresponding to the audio signals of each channel collected in the same period and the collection distance of the audio signals of the channels, and the The phase in the sparse transformation coefficients corresponding to the audio signals of each channel varies linearly with the collection distance of the audio signals of the channels, and the supports in the sparse transformation coefficients corresponding to the audio signals of each channel collected in the same period are the same.

The time-domain near-field algorithm of the audio signal may specifically include: linear correlation of amplitudes in the sparse transformation coefficients corresponding to the audio signals of the same channel collected in different time periods; The phase of the same channel is the same, and the supports in the sparse transform coefficients corresponding to the audio signals of the same channel collected in different time periods are Bernoulli distribution.

S308, the second terminal performs sparse inverse transformation on the sparse transform coefficient corresponding to the audio signal whose next channel of the channel is located in the specified frame, to obtain the audio signal whose next channel of the channel is located in the specified frame.

In the audio signal reconstruction method shown in FIG. 3 , the first terminal samples the audio signals of each channel collected in the same period, and the first terminal performs sparse transformation and perceptual measurement on the sampled audio signals of each channel to obtain compressed data, After acquiring the compressed data sent by the first terminal, the second terminal determines, according to the sparse transform coefficients of one channel corresponding to the audio signal located in the specified frame, the prior sparse transform coefficients corresponding to the audio signal located in the specified frame of the next channel of the channel , according to the a priori sparse transform coefficients corresponding to the audio signal whose next channel is located in the specified frame, the measurement data corresponding to the audio signal whose next channel is located in the specified frame, and the measurement matrix, obtain the next channel of the channel located in The sparse transformation coefficient corresponding to the audio signal of the specified frame, perform sparse inverse transformation on the sparse transformation coefficient corresponding to the audio signal whose next channel of the channel is located in the specified frame, and obtain the audio signal whose next channel of this channel is located in the specified frame, which can improve the quality of the audio signal.

Referring to FIG. 4, FIG. 4 is a schematic flowchart of an audio signal compression method provided in another embodiment of the present invention. As shown in the figure, the audio signal compression method in the embodiment of the present invention may at least include:

S401, the terminal collects audio signals of multiple channels.

S402, the terminal obtains the audio signals of each channel collected in the same time period through cosine window sampling.

The terminal can obtain the audio signals of each channel collected in the same period through cosine window sampling. Taking the interface schematic diagram of the sampled audio signal shown in FIG. 1D as an example, the terminal collects audio signals of K channels, and the frame length of each collected audio signal is t, then the terminal can sample through the cosine window to obtain K channels are located at the starting point. The audio signal of the first frame, optionally, the terminal can obtain the audio signal of the t-th frame with K channels by sampling through the cosine window, and so on.

Optionally, after the terminal obtains the audio signals of each channel collected in the same time period through cosine window sampling, the terminal may store the sampled audio signals of each channel collected in the same time period into a preset buffer area. In addition, when the data volume of the audio signals in the preset buffer area is greater than the capacity of the preset buffer area, the terminal can obtain the storage time of each audio signal, and delete the audio signal with the earliest storage time.

S403: The terminal performs sparse transformation on the sampled audio signals of each channel to obtain sparse transformation coefficients.

S404, the terminal multiplies the preset measurement matrix by the sparse transformation coefficient to obtain a perceptual measurement value.

S405, the terminal adds the sensing measurement value and the noise vector to obtain compressed data.

S406, the terminal determines whether the sampled audio signal is located in the last frame.

The terminal can determine whether to sample the audio signal of each channel located in the last frame, and when the sampled audio signal is not located in the last frame, the terminal can further perform step S407; when the sampled audio signal is located in the last frame, the terminal can Step S408 is further executed.

S407, when the sampled audio signal is not located in the last frame, the terminal shifts the cosine window.

For example, the first terminal obtains audio signals whose channels are located in the initial frame through cosine window sampling, and after obtaining the compressed sampling data of each channel located in the initial frame, when the sampled audio signal is not located in the last frame, the first terminal can The cosine window is shifted, and steps S402 to S405 are further performed, that is, the audio signals of each channel located in the second frame are sampled, and the audio signals of each channel located in the second frame are subjected to sparse transformation and perceptual measurement. Compressed data of two frames; when the sampled audio signal is not in the last frame, the terminal can shift the cosine window to obtain the audio signal of each channel in the third frame by sampling, and perform the audio signal of each channel in the third frame. Through sparse transformation and perceptual measurement, the compressed data of each channel in the third frame is obtained until the compressed data of each channel in the last frame is obtained.

S408, when the sampled audio signal is in the last frame, the terminal outputs an end flag.

When the audio signal sampled by the terminal through the cosine window is located in the last frame, the terminal can output an end flag to trigger the terminal to send the compressed data whose channels are located in different frames to other terminals. Optionally, the terminal may send the compressed data to other terminals after processing the audio signals whose channels are located in the same frame to obtain compressed data whose channels are located in the same frame.

Exemplarily, the terminal may collect sound sources through an array composed of 32 microphones to obtain audio signals of 32 channels, and the frame length of the collected audio signals of each channel is 16384. The cosine windows are 50% overlapped. The preset measurement matrix can be a normalized Gaussian random matrix of 5641*16384, the noise vector corresponding to the acquisition environment of the audio signal can be Gaussian white noise with a mean of 0 and a variance of 1, and the data volume of the preset buffer area is 16384* 32B, the compression ratio in the process of compressing the sampled data obtained by the terminal processing the audio signal is 1:3, that is, when the data volume of the audio signal whose channel is located in the specified frame is 300KB, the compression ratio of the compressed sampled data obtained by processing the audio signal is 1:3. The amount of data is 100KB.

In the audio signal compression method shown in FIG. 4 , the terminal obtains the audio signals of each channel collected in the same time period through cosine window sampling, and performs sparse transformation on the sampled audio signals of each channel to obtain sparse transformation coefficients, and the preset The measurement matrix is multiplied by the sparse transformation coefficient to obtain the perceptual measurement value, the perceptual measurement value is added to the noise vector, the compressed sampling data is obtained, and the cosine window is shifted to obtain the audio signal of each channel located in the next frame of the specified frame, and then The compressed data of each channel located in the next frame of the specified frame can be obtained, and an effective sparse model can be established to compress the audio signal.

Referring to FIG. 5, FIG. 5 is a schematic structural diagram of an audio signal reconstruction apparatus provided in an embodiment of the present invention, wherein the audio signal reconstruction apparatus provided in an embodiment of the present invention may at least include a compressed data acquisition module 501, an inverse quantization module 502, Joint reconstruction module 503 and sparse inverse transformation module 504, wherein:

A compressed data acquisition module 501, configured to acquire compressed data corresponding to at least two audio signals;

an inverse quantization module 502, configured to perform inverse quantization on the compressed data corresponding to the at least two audio signals, thereby obtaining measurement data corresponding to the at least two audio signals;

a joint reconstruction module 503, configured to obtain a measurement matrix, and jointly reconstruct the sparse transform coefficients corresponding to the at least two audio signals according to the measurement data corresponding to the at least two audio signals and the measurement matrix;

The sparse inverse transform module 504 is configured to perform sparse inverse transform on the sparse transform coefficients corresponding to the at least two audio signals to obtain the at least two audio signals.

Optionally, the at least two audio signals include a first audio signal and a second audio signal, and the joint reconstruction module 503 is specifically used for:

The sparse transformation coefficient corresponding to the second audio signal is calculated according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix.

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The joint reconstruction module 503 calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically used for :

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal is determined, and the difference between the first amplitude and the second amplitude is The ratio is the ratio of the logarithm of the distance between the microphone corresponding to the first audio signal and the sound source and the logarithm of the distance between the microphone corresponding to the second audio signal and the sound source;

The second amplitude is used as a priori of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding The magnitudes in the sparse transform coefficients of .

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The joint reconstruction module 503 calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically used for :

According to the first phase in the sparse transform coefficient corresponding to the first audio signal, the second phase in the prior sparse transform coefficient corresponding to the second audio signal is determined, and the difference between the first phase and the second phase is The ratio is the ratio of the distance between the microphone corresponding to the first audio signal and the sound source and the distance between the microphone corresponding to the second audio signal and the sound source;

Taking the second phase as a priori of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the corresponding measurement data of the second audio signal and the measurement matrix according to the second audio signal The phase in the sparse transform coefficients of .

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The joint reconstruction module 503 calculates the sparse transformation coefficient corresponding to the second audio signal according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically used for :

determining the first frequency in the sparse transform coefficient corresponding to the first audio signal as the second frequency in the prior sparse transform coefficient corresponding to the second audio signal;

The second frequency is used as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding frequency of the second audio signal. The frequencies in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired in different time periods, and the joint reconstruction module 503 According to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, calculate the sparse transformation coefficient corresponding to the second audio signal, which is specifically used for:

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal is determined, which corresponds to the audio signals of the same channel in different time periods. The amplitudes in the coefficient transform coefficients have a linear relationship with the sequence numbers of the frames corresponding to the audio signals of different time periods;

The second amplitude is used as a priori of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding The magnitudes in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired in different time periods, and the joint reconstruction module 503 According to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, calculate the sparse transformation coefficient corresponding to the second audio signal, which is specifically used for:

determining the first phase in the sparse transform coefficient corresponding to the first audio signal as the second phase in the prior sparse transform coefficient corresponding to the second audio signal;

Taking the second phase as a priori of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the corresponding measurement data of the second audio signal and the measurement matrix according to the second audio signal The phase in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired in different time periods, and the joint reconstruction module 503 According to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, calculate the sparse transformation coefficient corresponding to the second audio signal, which is specifically used for:

According to the first frequency in the sparse transform coefficient corresponding to the first audio signal, the second frequency in the prior sparse transform coefficient corresponding to the second audio signal is determined, and the first frequency and the second frequency have an intersection , the frequencies in the intersection set are obtained by randomly selecting the frequencies in the first frequency;

The second frequency is used as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding frequency of the second audio signal. The frequencies in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal are audio signals collected in adjacent time periods.

Optionally, the at least two audio signals include a third audio signal, and the joint reconstruction module 503 jointly reconstructs the at least two audio signals according to the measurement data corresponding to the at least two audio signals and the measurement matrix The corresponding sparse transform coefficients, specifically for:

The sparse transformation coefficient corresponding to the third audio signal is calculated according to the preset initial sparse transformation coefficient, the measurement data corresponding to the third audio signal, and the measurement matrix.

Specifically, the audio signal reconstruction apparatus introduced in the embodiments of the present invention may be used to implement part or all of the processes in the audio signal reconstruction method embodiments of the present invention introduced in conjunction with FIG. 2 to FIG. 4 .

Referring to FIG. 6, FIG. 6 is a schematic structural diagram of a terminal provided in an embodiment of the present invention. As shown in FIG. 6 , the terminal may include: a processor 601 , a memory 602 and a network interface 603 . The processor 601 is connected to the memory 602 and the network interface 603. For example, the processor 601 may be connected to the memory 602 and the network interface 603 through a bus.

The processor 601 may be a central processing unit (central processing unit, CPU), a network processor (network processor, NP), or the like.

The memory 602 can specifically be used to store audio signals of multiple channels and the like. The memory 602 may include volatile memory (volatile memory), such as random-access memory (RAM); the memory may also include non-volatile memory (non-volatile memory), such as read-only memory (read-only memory) only memory, ROM), flash memory (flash memory), hard disk drive (HDD) or solid-state drive (solid-state drive, SSD); the memory may also include a combination of the above-mentioned types of memory.

The network interface 603 is used to communicate with other terminals, for example, to receive compressed data sent by other terminals. Optionally, the network interface 603 may include a standard wired interface, a wireless interface (such as a WI-FI interface), and the like.

obtain compressed data corresponding to at least two audio signals;

performing inverse quantization on the compressed data corresponding to the at least two audio signals, thereby obtaining measurement data corresponding to the at least two audio signals;

acquiring a measurement matrix, and jointly reconstructing sparse transform coefficients corresponding to the at least two audio signals according to the measurement data corresponding to the at least two audio signals and the measurement matrix;

Sparse inverse transform is performed on the sparse transform coefficients corresponding to the at least two audio signals to obtain the at least two audio signals.

Optionally, the at least two audio signals include a first audio signal and a second audio signal, and the at least two audio signals are jointly reconstructed according to the measurement data corresponding to the at least two audio signals and the measurement matrix. The sparse transform coefficients corresponding to the signal include:

The sparse transformation coefficient corresponding to the second audio signal is calculated according to the sparse transformation coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix.

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The calculation of the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix includes:

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal is determined, and the difference between the first amplitude and the second amplitude is The ratio is the ratio of the logarithm of the distance between the microphone corresponding to the first audio signal and the sound source and the logarithm of the distance between the microphone corresponding to the second audio signal and the sound source;

The second amplitude is used as a priori of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding The magnitudes in the sparse transform coefficients of .

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The calculation of the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix includes:

According to the first phase in the sparse transform coefficient corresponding to the first audio signal, the second phase in the prior sparse transform coefficient corresponding to the second audio signal is determined, and the difference between the first phase and the second phase is The ratio is the ratio of the distance between the microphone corresponding to the first audio signal and the sound source and the distance between the microphone corresponding to the second audio signal and the sound source;

Taking the second phase as a priori of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the corresponding measurement data of the second audio signal and the measurement matrix according to the second audio signal The phase in the sparse transform coefficients of .

Optionally, the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, and the first audio signal and the second audio signal are audio signals collected in the same time period, so The calculation of the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix includes:

determining the first frequency in the sparse transform coefficient corresponding to the first audio signal as the second frequency in the prior sparse transform coefficient corresponding to the second audio signal;

The second frequency is used as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding frequency of the second audio signal. The frequencies in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals collected in different time periods, and the The sparse transform coefficient corresponding to an audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and calculating the sparse transform coefficient corresponding to the second audio signal includes:

According to the first amplitude in the sparse transformation coefficient corresponding to the first audio signal, the second amplitude in the prior sparse transformation coefficient corresponding to the second audio signal is determined, which corresponds to the audio signals of the same channel in different time periods. The amplitudes in the coefficient transform coefficients have a linear relationship with the sequence numbers of the frames corresponding to the audio signals of different time periods;

The second amplitude is used as a priori of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding The magnitudes in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals collected in different time periods, and the The sparse transform coefficient corresponding to an audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and calculating the sparse transform coefficient corresponding to the second audio signal includes:

determining the first phase in the sparse transform coefficient corresponding to the first audio signal as the second phase in the prior sparse transform coefficient corresponding to the second audio signal;

Taking the second phase as a priori of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the corresponding measurement data of the second audio signal and the measurement matrix according to the second audio signal The phase in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals collected in different time periods, and the The sparse transform coefficient corresponding to an audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and calculating the sparse transform coefficient corresponding to the second audio signal includes:

According to the first frequency in the sparse transform coefficient corresponding to the first audio signal, the second frequency in the prior sparse transform coefficient corresponding to the second audio signal is determined, and the first frequency and the second frequency have an intersection , the frequencies in the intersection set are obtained by randomly selecting the frequencies in the first frequency;

The second frequency is used as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and the corresponding measurement data of the second audio signal and the measurement matrix are used to calculate the corresponding frequency of the second audio signal. The frequencies in the sparse transform coefficients of .

Optionally, the first audio signal and the second audio signal are audio signals collected in adjacent time periods.

Optionally, the at least two audio signals include a third audio signal, and the sparse transform corresponding to the at least two audio signals is jointly reconstructed according to the measurement data corresponding to the at least two audio signals and the measurement matrix. Factors include:

The sparse transformation coefficient corresponding to the third audio signal is calculated according to the preset initial sparse transformation coefficient, the measurement data corresponding to the third audio signal, and the measurement matrix.

Specifically, the terminal introduced in the embodiments of the present invention may be used to implement part or all of the processes in the embodiments of the audio signal reconstruction method described in conjunction with FIG. 2 to FIG. 4 of the present invention.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structures, materials, or features are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined.

Logic and/or steps represented in flowcharts or otherwise described herein, for example, may be considered a program listing of executable instructions for implementing the logical functions, and may be embodied in any computer-readable medium to For use with an instruction execution system, apparatus or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions from an instruction execution system, apparatus or apparatus) or in conjunction with such instruction execution system, apparatus or apparatus equipment used. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or apparatus. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or more wiring (electronic devices), portable computer disk cartridges (magnetic devices), random access memory, read only memory , Erasable Editable ROM, Fiber Optic Devices, and Portable Optical ROM. In addition, the computer readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, followed by editing, interpretation, or other suitable medium as necessary process to obtain the program electronically and then store it in computer memory.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following techniques known in the art: Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, programmable gate arrays, field programmable gate arrays, etc.

In addition, the modules in the various embodiments of the present invention may be implemented in the form of hardware, and may also be implemented in the form of software function modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (22)

1. A method of audio signal reconstruction, comprising:

acquiring compressed data corresponding to at least two audio signals;

inversely quantizing the compressed data corresponding to the at least two audio signals to obtain measured data corresponding to the at least two audio signals;

acquiring a measurement matrix, and jointly reconstructing sparse transform coefficients corresponding to the at least two audio signals according to measurement data corresponding to the at least two audio signals and the measurement matrix;

performing sparse inverse transformation on sparse transformation coefficients corresponding to the at least two audio signals to obtain the at least two audio signals;

wherein the at least two audio signals include a first audio signal and a second audio signal, and the jointly reconstructing sparse transform coefficients corresponding to the at least two audio signals according to the measurement data corresponding to the at least two audio signals and the measurement matrix includes:

and calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal and the measurement matrix.

2. The method of claim 1, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in the same time period, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a second amplitude in a priori sparse transform coefficient corresponding to the second audio signal according to a first amplitude in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first amplitude to the second amplitude is the ratio of the logarithm of the distance from a microphone corresponding to the first audio signal to a sound source and the logarithm of the distance from the microphone corresponding to the second audio signal to the sound source;

and taking the second amplitude as the prior of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and calculating the amplitude in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

3. The method of claim 1, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in the same time period, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a second phase in a sparse transform coefficient corresponding to the second audio signal according to a first phase in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first phase to the second phase is the ratio of the distance from a microphone corresponding to the first audio signal to a sound source to the distance from a microphone corresponding to the second audio signal to the sound source;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

4. The method of claim 2, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in the same time period, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a second phase in a sparse transform coefficient corresponding to the second audio signal according to a first phase in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first phase to the second phase is the ratio of the distance from a microphone corresponding to the first audio signal to a sound source to the distance from a microphone corresponding to the second audio signal to the sound source;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

5. The method according to any one of claims 1 to 4, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in the same time period, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a first frequency in a sparse transform coefficient corresponding to the first audio signal as a second frequency in a prior sparse transform coefficient corresponding to the second audio signal;

and taking the second frequency as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and calculating the frequency in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

6. The method according to claim 1, wherein the first audio signal and the second audio signal correspond to a same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a second amplitude in the prior sparse transform coefficient corresponding to the second audio signal according to a first amplitude in the sparse transform coefficient corresponding to the first audio signal, wherein the amplitudes in the coefficient transform coefficients corresponding to the audio signals of different time periods corresponding to the same channel are in a linear relation with the sequence numbers of the frames corresponding to the audio signals of different time periods;

and taking the second amplitude as the prior of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and calculating the amplitude in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

7. The method according to claim 1 or 6, wherein the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix comprises:

determining a first phase in a sparse transform coefficient corresponding to the first audio signal as a second phase in a prior sparse transform coefficient corresponding to the second audio signal;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

8. An audio signal reconstruction method, characterized in that the method includes all the features of the method of any one of claims 1, 6 and 7, and the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the calculating of the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix includes:

determining a second frequency in a priori sparse transform coefficient corresponding to the second audio signal according to a first frequency in the sparse transform coefficient corresponding to the first audio signal, wherein the first frequency and the second frequency have an intersection, and the frequencies in the intersection are obtained by randomly selecting the frequencies in the first frequency;

and taking the second frequency as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and calculating the frequency in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

9. A method of audio signal reconstruction, characterized in that the method comprises all the features of the method of any one of claims 6 to 8, and in that the first audio signal and the second audio signal are audio signals acquired in adjacent time periods.

10. A method for audio signal reconstruction, wherein the method comprises all the features of the method of any one of claims 1 to 9, and wherein the at least two audio signals comprise a third audio signal, and wherein the jointly reconstructing sparse transform coefficients corresponding to the at least two audio signals from the measurement data corresponding to the at least two audio signals and the measurement matrix comprises:

and calculating a sparse transform coefficient corresponding to the third audio signal according to a preset initial sparse transform coefficient, the measurement data corresponding to the third audio signal and the measurement matrix.

11. An audio signal reconstruction apparatus, comprising:

the compressed data acquisition module is used for acquiring compressed data corresponding to at least two audio signals;

the inverse quantization module is used for performing inverse quantization on the compressed data corresponding to the at least two audio signals so as to obtain the measurement data corresponding to the at least two audio signals;

the joint reconstruction module is used for acquiring a measurement matrix and jointly reconstructing sparse transform coefficients corresponding to the at least two audio signals according to the measurement data corresponding to the at least two audio signals and the measurement matrix;

the sparse inverse transformation module is used for carrying out sparse inverse transformation on sparse transformation coefficients corresponding to the at least two audio signals to obtain the at least two audio signals;

wherein the at least two audio signals comprise a first audio signal and a second audio signal, and the joint reconstruction module is specifically configured to:

and calculating the sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal and the measurement matrix.

12. The apparatus according to claim 11, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in a same time period, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a second amplitude in a priori sparse transform coefficient corresponding to the second audio signal according to a first amplitude in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first amplitude to the second amplitude is the ratio of the logarithm of the distance from a microphone corresponding to the first audio signal to a sound source and the logarithm of the distance from the microphone corresponding to the second audio signal to the sound source;

and taking the second amplitude as the prior of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and calculating the amplitude in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

13. The apparatus according to claim 11, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in a same time period, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a second phase in a sparse transform coefficient corresponding to the second audio signal according to a first phase in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first phase to the second phase is the ratio of the distance from a microphone corresponding to the first audio signal to a sound source to the distance from a microphone corresponding to the second audio signal to the sound source;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

14. The apparatus according to claim 12, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in a same time period, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a second phase in a sparse transform coefficient corresponding to the second audio signal according to a first phase in the sparse transform coefficient corresponding to the first audio signal, wherein the ratio of the first phase to the second phase is the ratio of the distance from a microphone corresponding to the first audio signal to a sound source to the distance from a microphone corresponding to the second audio signal to the sound source;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

15. The apparatus according to any one of claims 11 to 14, wherein the first audio signal corresponds to a first channel, the second audio signal corresponds to a second channel, the first audio signal and the second audio signal are audio signals acquired in the same time period, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a first frequency in a sparse transform coefficient corresponding to the first audio signal as a second frequency in a prior sparse transform coefficient corresponding to the second audio signal;

and taking the second frequency as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and calculating the frequency in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

16. The apparatus according to claim 11, wherein the first audio signal and the second audio signal correspond to a same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to a sparse transform coefficient corresponding to the first audio signal, measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a second amplitude in the prior sparse transform coefficient corresponding to the second audio signal according to a first amplitude in the sparse transform coefficient corresponding to the first audio signal, wherein the amplitudes in the coefficient transform coefficients corresponding to the audio signals of different time periods corresponding to the same channel are in a linear relation with the sequence numbers of the frames corresponding to the audio signals of different time periods;

and taking the second amplitude as the prior of the amplitude in the sparse transform coefficient corresponding to the second audio signal, and calculating the amplitude in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

17. The apparatus according to claim 11 or 16, wherein the first audio signal and the second audio signal correspond to a same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to a sparse transform coefficient corresponding to the first audio signal, measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a first phase in a sparse transform coefficient corresponding to the first audio signal as a second phase in a prior sparse transform coefficient corresponding to the second audio signal;

and taking the second phase as the prior of the phase in the sparse transform coefficient corresponding to the second audio signal, and calculating the phase in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

18. An audio signal reconstruction apparatus, characterized in that the apparatus includes all the features of the apparatus of claims 11, 16 and 17, and the first audio signal and the second audio signal correspond to the same channel, the first audio signal and the second audio signal are audio signals acquired at different time intervals, and the joint reconstruction module calculates a sparse transform coefficient corresponding to the second audio signal according to the sparse transform coefficient corresponding to the first audio signal, the measurement data corresponding to the second audio signal, and the measurement matrix, and is specifically configured to:

determining a second frequency in a priori sparse transform coefficient corresponding to the second audio signal according to a first frequency in the sparse transform coefficient corresponding to the first audio signal, wherein the first frequency and the second frequency have an intersection, and the frequencies in the intersection are obtained by randomly selecting the frequencies in the first frequency;

and taking the second frequency as the prior of the frequency in the sparse transform coefficient corresponding to the second audio signal, and calculating the frequency in the sparse transform coefficient corresponding to the second audio signal according to the measurement data corresponding to the second audio signal and the measurement matrix.

19. An apparatus for reconstructing an audio signal, the apparatus comprising all the features of the apparatus of any one of claims 16 to 18, wherein the first audio signal and the second audio signal are audio signals acquired in adjacent time periods.

20. An audio signal reconstruction apparatus, characterized in that said apparatus comprises all the features of the apparatus of any one of claims 11 to 19, and said at least two audio signals comprise a third audio signal, and said joint reconstruction module jointly reconstructs sparse transform coefficients corresponding to said at least two audio signals according to the measurement data corresponding to said at least two audio signals and said measurement matrix, in particular to:

and calculating a sparse transform coefficient corresponding to the third audio signal according to a preset initial sparse transform coefficient, the measurement data corresponding to the third audio signal and the measurement matrix.

21. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a computer device, is adapted to implement the method of any one of claims 1 to 10.

22. An audio signal reconstruction apparatus, comprising:

a processor and a memory, wherein the processor is capable of processing a plurality of data,

wherein the memory stores a computer program for implementing the method of any one of claims 1 to 10 when executed by the processor.

Images