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CN114974287B - Wind noise reduction method and device, terminal equipment and storage medium - Google Patents

Wind noise reduction method and device, terminal equipment and storage medium

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Publication number
CN114974287B
CN114974287B CN202210327843.7A CN202210327843A CN114974287B CN 114974287 B CN114974287 B CN 114974287B CN 202210327843 A CN202210327843 A CN 202210327843A CN 114974287 B CN114974287 B CN 114974287B
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wind noise
audio signal
category
identification
preset
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CN114974287A (en
Inventor
邱志豪
陈建义
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Xiamen Yealink Network Technology Co Ltd
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Xiamen Yealink Network Technology Co Ltd
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/02—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using spectral analysis, e.g. transform vocoders or subband vocoders
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/26—Pre-filtering or post-filtering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/21—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being power information
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3025—Determination of spectrum characteristics, e.g. FFT
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3028—Filtering, e.g. Kalman filters or special analogue or digital filters
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02163—Only one microphone

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Quality & Reliability (AREA)
  • Circuit For Audible Band Transducer (AREA)

Abstract

The application discloses a wind noise reduction method, a device, a terminal device and a storage medium, which are used for carrying out signal analysis on audio signals acquired by microphones by utilizing a plurality of preset signal analysis algorithms, outputting a plurality of wind noise identification identifiers, wherein the microphones comprise single microphones and determine wind noise types of the audio signals based on the plurality of wind noise identification identifiers, determining target wind noise filters corresponding to the wind noise types based on the wind noise types of the audio signals, carrying out noise reduction on the audio signals by utilizing the target wind noise filters, and outputting the noise-reduced target audio signals, so that the wind noise types of the audio signals are classified and identified, corresponding noise reduction processing is carried out on the audio signals of different wind noise types in a targeted manner, meanwhile, the signal analysis is carried out by utilizing a plurality of preset signal analysis algorithms, the condition of omission or false detection can be effectively avoided, the accuracy of the classification and identification of the audio signals is improved, and the noise reduction effect is further improved.

Description

Wind noise reduction method and device, terminal equipment and storage medium
Technical Field
The present application relates to the field of audio signal processing technologies, and in particular, to a method and apparatus for reducing wind noise, a terminal device, and a storage medium.
Background
Wind noise exists in life and work scenes of people, such as fan sound, air conditioner sound, wind noise caused by walking and the like, and when a user calls, the wind noise often affects the call quality, so that the wind noise needs to be reduced for call audio.
At present, a wind noise reduction scheme is mainly realized based on multiple microphones, a plurality of audio signals acquired by the multiple microphones are subjected to signal analysis to analyze difference information between human voice and wind noise, the difference information is utilized to determine the noise reduction gain of the multiple microphones, and finally, the input frequency domain signals are subjected to noise reduction processing according to the noise reduction gain. However, for a single microphone, the difference information between the voice and the wind noise which can be acquired is less, the voice and the wind noise are difficult to distinguish by utilizing the difference information, and the wind noise is easy to miss detection or false detection. It can be seen that the current wind noise reduction scheme applied to a single microphone has a problem of poor noise reduction effect.
Disclosure of Invention
The application provides a method, a device, terminal equipment and a storage medium for reducing wind noise, which are used for improving the wind noise identification accuracy of a single microphone, so as to improve the noise reduction effect.
In order to solve the above technical problems, an embodiment of the present application provides a method for reducing wind noise, including:
Based on a plurality of preset signal analysis algorithms, performing signal analysis on the audio signals acquired by the microphones, and outputting a plurality of wind noise identification marks, wherein the wind noise identification marks are used for representing wind noise identification results corresponding to each preset signal analysis algorithm, and the microphones comprise single microphones;
Based on the plurality of wind noise identification identifiers, determining wind noise categories of the audio signals, wherein the wind noise categories comprise pure wind noise and wind noise-containing human voice;
Determining a target wind noise filter corresponding to a wind noise class based on the wind noise class of the audio signal;
and (3) using the target wind noise filter to reduce noise of the audio signal and outputting the target audio signal after noise reduction.
According to the method, the device and the system, the audio signals collected by the microphones are subjected to signal analysis by utilizing a plurality of preset signal analysis algorithms, a plurality of wind noise identification marks are output, the microphones comprise a single microphone, the wind noise types of the audio signals are determined based on the plurality of wind noise identification marks, so that the wind noise types of the audio signals are classified and identified, corresponding noise reduction processing is conveniently carried out on the audio signals of different wind noise types in a targeted manner, meanwhile, the signal analysis is carried out by utilizing the plurality of preset signal analysis algorithms, the condition of missed detection or false detection can be effectively avoided, the accuracy of classifying and identifying the audio signals is improved, the noise reduction effect is further improved, the target wind noise filter corresponding to the wind noise type is determined based on the wind noise type of the audio signals, the target audio signals after noise reduction are output by utilizing the target wind noise filter, and accordingly the audio signals of different wind noise types can be subjected to noise reduction processing in a targeted manner, and the noise reduction effect is improved.
In an embodiment, the preset signal analysis algorithm includes a low frequency power ratio algorithm, a power spectrum difference algorithm and an LPC analysis algorithm, performs signal analysis on an audio signal collected by a microphone based on a plurality of preset signal analysis algorithms, and outputs a plurality of wind noise identification marks, including:
based on a low-frequency power ratio algorithm, carrying out low-frequency power ratio analysis on the audio signal, and outputting a first wind noise identification mark;
Based on a power spectrum difference algorithm, performing power spectrum analysis on the audio signal, and outputting a second wind noise identification mark;
and performing LPC analysis on the audio signal based on an LPC analysis algorithm, and outputting a third wind noise identification mark.
In an alternative embodiment, based on a low frequency power ratio algorithm, the low frequency power ratio analysis is performed on the audio signal, and a first wind noise identification identifier is output, including:
calculating low-frequency energy and full-band energy of the audio signal based on a low-frequency power ratio algorithm;
Calculating the low-frequency energy power ratio of the audio signal according to the low-frequency energy and the full-frequency band energy;
and determining a first wind noise identification mark of the audio signal according to the low-frequency energy power ratio.
In an alternative embodiment, based on a power spectrum difference algorithm, performing power spectrum analysis on the audio signal, and outputting a second wind noise identification, including:
Determining the power spectrum of each frequency point of the audio signal in a preset frequency range based on a power spectrum difference algorithm;
According to the power spectrum of each frequency point, calculating the power spectrum difference value of the audio signal in a preset frequency range;
And determining a second wind noise identification mark of the audio signal according to the power spectrum difference value.
In an alternative embodiment, the LPC analysis is performed on the audio signal based on an LPC analysis algorithm, and the third wind noise identification is output, including:
determining second-order LPC analysis resonance peak points of the audio signal based on an LPC analysis algorithm;
inputting second-order LPC analysis resonance peak points into a preset LPC analysis polynomial to obtain a polynomial value;
and determining a third wind noise identification mark of the audio signal according to the polynomial value.
In one embodiment, determining a wind noise class of an audio signal based on a plurality of wind noise identification identifiers comprises:
Based on the plurality of wind noise identification identifiers, identifying audio categories of the audio signals, wherein the audio categories comprise pure human voice, pure wind noise and wind noise-containing human voice;
If the audio class of the audio signal is not pure wind noise or wind noise-containing human voice, determining whether a wind noise identification delay value is larger than a preset threshold value, wherein the wind noise identification delay value is used for representing whether the wind noise class of the last audio signal continuous with the audio signal is pure wind noise or wind noise-containing human voice;
if the wind noise identification delay value is larger than the preset threshold value, judging that the wind noise type of the audio signal is pure wind noise or wind noise-containing human voice.
In an alternative embodiment, the wind noise identification mark comprises a first wind noise identification mark obtained based on analysis of a low-frequency power ratio algorithm, a second wind noise identification mark obtained based on analysis of a power spectrum difference algorithm, and a third wind noise identification mark obtained based on analysis of an LPC analysis algorithm, and the audio class of the audio signal is identified based on the plurality of wind noise identification marks, and the method comprises the following steps:
If the first wind noise identification mark is not the first preset mark, the second wind noise identification mark is not the second preset mark and the third wind noise identification mark is not the third preset mark, judging that the audio class of the audio signal is pure voice;
If the first wind noise identification mark is a first preset mark, the second wind noise identification mark is a second preset mark or the third wind noise identification mark is a third preset mark, judging that the audio class of the audio signal is the voice containing wind noise;
If the first wind noise identification mark is a first preset mark, the second wind noise identification mark is a second preset mark and the third wind noise identification mark is a third preset mark, and the high-frequency energy ratio of the audio signal is smaller than the preset energy ratio, the audio category of the audio signal is judged to be pure wind noise.
In an alternative embodiment, after identifying the audio category of the audio signal based on the plurality of wind noise identification identifiers, further comprising:
If the audio class of the audio signal is pure wind noise or wind noise-containing human voice, adding the wind noise identification delay value with a preset value to obtain the latest wind noise identification delay value.
In an alternative embodiment, if the audio class of the audio signal is not pure wind noise or wind noise-containing human voice, determining whether the wind noise identification delay value is greater than a preset threshold value further includes:
if the wind noise identification delay value is not greater than the preset threshold value, setting the wind noise identification delay value as the preset threshold value to obtain the latest wind noise identification delay value.
In an embodiment, determining a target wind noise filter corresponding to a wind noise class based on a wind noise class of an audio signal includes:
if the wind noise class of the audio signal is pure wind noise, determining that the target wind noise filter is a single wind noise filter, wherein the single wind noise filter is used for wind noise filtering of a low frequency band of the audio signal;
if the wind noise class of the audio signal is wind noise-containing human voice, determining that the target wind noise filter is a combined wind noise filter, wherein the combined wind noise filter is used for carrying out wind noise filtering with different attenuation degrees on each frequency band of the audio signal.
In an alternative embodiment, if the wind noise class of the audio signal is pure wind noise, before determining that the target wind noise filter is a single wind noise filter, the method further includes:
And carrying out Euler transformation on a Laplacian domain of the preset LPC filter to obtain the single wind noise filter.
In a second aspect, an embodiment of the present application provides a wind noise reduction apparatus, including:
the analysis module is used for carrying out signal analysis on the audio signals acquired by the microphone based on a plurality of preset signal analysis algorithms, outputting a plurality of wind noise identification marks, wherein the wind noise identification marks are used for representing wind noise identification results corresponding to each preset signal analysis algorithm, and the microphone comprises a single microphone;
the first determining module is used for determining wind noise categories of the audio signals based on the plurality of wind noise identification marks, wherein the wind noise categories comprise pure wind noise and wind noise-containing human voice;
the second determining module is used for determining a target wind noise filter corresponding to the wind noise category based on the wind noise category of the audio signal;
and the noise reduction module is used for reducing noise of the audio signal by utilizing the target wind noise filter and outputting the noise-reduced target audio signal.
In a third aspect, an embodiment of the present application provides a terminal device, including a processor and a memory, where the memory is configured to store a computer program, and the computer program when executed by the processor implements the steps of the wind noise reduction method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer readable storage medium storing a computer program, which when executed by a processor implements the steps of the wind noise reduction method as in the first aspect.
It should be noted that, the beneficial effects of the second aspect to the fourth aspect are referred to the related description of the first aspect, and are not repeated here.
Drawings
FIG. 1 is a flow chart of a method for reducing wind noise according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for reducing wind noise according to another embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for reducing wind noise according to another embodiment of the present application;
FIG. 4 is a flowchart illustrating a method for reducing wind noise according to another embodiment of the present application;
fig. 5 is a schematic structural diagram of a wind noise reduction device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
As described in the related art, the single microphone has little difference information between the human voice and the wind noise that can be acquired, and it is difficult to distinguish the human voice from the wind noise by using the difference information, and it is easy to cause missing detection or false detection of the wind noise. It can be seen that the current wind noise reduction scheme applied to a single microphone has a problem of poor noise reduction effect.
Therefore, the embodiment of the application provides a wind noise reduction method, device, terminal equipment and storage medium, which are used for carrying out signal analysis on audio signals collected by a microphone by utilizing a plurality of preset signal analysis algorithms, outputting a plurality of wind noise identification identifiers, wherein the microphone comprises a single microphone, determining wind noise types of the audio signals based on the plurality of wind noise identification identifiers, and accordingly classifying and identifying the wind noise types of the audio signals, so that corresponding noise reduction processing is carried out on the audio signals of different wind noise types in a subsequent targeted manner, meanwhile, signal analysis is carried out by utilizing a plurality of preset signal analysis algorithms, the condition of omission or false detection can be effectively avoided, the accuracy of classifying and identifying the audio signals is improved, and further the noise reduction effect is improved.
Referring to fig. 1, a flow chart of a method for reducing wind noise according to an embodiment of the application is shown. The method for reducing the wind noise can be applied to terminal equipment, wherein the terminal equipment comprises, but is not limited to, equipment such as a smart phone, a tablet personal computer, a notebook computer, an earphone and the like which are internally provided with or externally connected with a microphone, and the microphone can be a single microphone or a plurality of microphones. As shown in fig. 1, the wind noise reduction method of the present embodiment includes steps S101 to S104, which are described in detail as follows:
Step S101, performing signal analysis on audio signals acquired by microphones based on a plurality of preset signal analysis algorithms, and outputting a plurality of wind noise identification marks, wherein the wind noise identification marks are used for representing wind noise identification results corresponding to each preset signal analysis algorithm, and the microphones comprise single microphones.
In this step, the audio signal is a continuous signal collected by the microphone. It can be appreciated that the present embodiment can reduce wind noise for a single microphone, and also can reduce wind noise for multiple microphones.
The preset signal analysis algorithm includes, but is not limited to, a low-frequency power ratio algorithm, a power spectrum difference algorithm and a linear prediction coefficient (Linear Prediction Coefficients, LPC) analysis algorithm, wherein the low-frequency power ratio algorithm is a method for identifying wind noise by using a low-frequency energy duty ratio of an audio signal, the power spectrum difference algorithm is a method for identifying wind noise by using different fluctuation differences of wind noise and human voice in a certain frequency domain range, and the LPC analysis algorithm is a method for identifying wind noise by using the characteristic that the wind noise has the same formants and the human voice has different formants.
Optionally, based on a low-frequency power ratio algorithm, a power spectrum difference algorithm and an LPC analysis algorithm, signal analysis is performed on the audio signal, and corresponding three wind noise identification identifiers are output. Illustratively, based on a low-frequency power ratio algorithm, signal analysis is performed on the audio signal, if wind noise exists in the audio signal, a wind noise identification flag wind_noise 1=1 is output, and if wind noise does not exist in the audio signal, a wind noise identification flag windn _ oise 1=0 is output.
Step S102, based on a plurality of wind noise identification marks, determining wind noise categories of the audio signals, wherein the wind noise categories comprise pure wind noise and wind noise-containing human voice.
In this step, the audio signal includes two cases of no wind noise and wind noise, wherein the wind noise category when wind noise exists includes pure wind noise and wind noise-containing human voice, the pure wind noise represents a signal segment of the audio signal in which only wind noise exists, and the wind noise-containing human voice represents a signal segment of the audio signal in which both wind noise and human voice exist.
Optionally, determining whether the audio signal has wind noise together through the plurality of wind noise identification identifiers, and if the audio signal has wind noise, continuing to determine the wind noise category of the audio signal according to the plurality of wind noise identification identifiers.
Step S103, determining a target wind noise filter corresponding to the wind noise category based on the wind noise category of the audio signal.
In this step, the target filter is a filter for noise reduction filtering of the current audio signal. Because the user sound exists in the noise-containing human voice, different filters are adopted to respectively reduce the noise of the pure wind noise and the audio signal of the noise-containing human voice in order to ensure the quality of the user sound after noise reduction.
Optionally, the target wind noise filter corresponding to the pure wind noise is a single wind noise filter which mainly carries out noise reduction filtering aiming at a single frequency band with wind noise, and the target wind noise filter corresponding to the wind noise-containing human noise is a combined wind noise filter which can carry out noise reduction filtering with different attenuation degrees aiming at a plurality of frequency bands.
Step S104, the target wind noise filter is utilized to reduce noise of the audio signal, and the noise-reduced target audio signal is output.
In this step, noise is reduced on a signal segment by a target wind noise filter corresponding to a wind noise class of the signal segment in the audio signal, noise reduction is performed on all wind noise signal segments of the audio signal by the corresponding target wind noise filters, and then the audio signal is subjected to operations such as inverse fourier transform and shift addition, and the target audio signal is output.
It will be appreciated that noise reduction filtering is not performed on a signal segment free of wind noise, i.e. a signal segment of pure human voice.
In an embodiment, fig. 2 is a schematic flow chart of a method for reducing wind noise according to another embodiment of the present application. As shown in fig. 2, the step S101 specifically includes steps S201 to S203. It will be appreciated that the same steps as in fig. 1 are not repeated here.
Step S201, based on the low frequency power ratio algorithm, performing low frequency power ratio analysis on the audio signal, and outputting a first wind noise identification.
In this step, since wind noise energy is mainly concentrated at low frequencies (below 400 Hz), and human voice energy is mainly and uniformly distributed at medium and low frequencies (below 800 Hz), wind noise can be identified by using the low frequency energy ratio.
Optionally, the low frequency energy and the full frequency band energy of the audio signal are calculated based on the low frequency power ratio algorithm, the low frequency energy power ratio of the audio signal is calculated according to the low frequency energy and the full frequency band energy, and the first wind noise identification of the audio signal is determined according to the low frequency energy power ratio.
In this alternative embodiment, the low frequency Energy l_energy (the sum of squares of the spectrum power below 300 Hz) and the whole band Energy t_energy of the audio signal are calculated, and then the low frequency Energy power ratio corresponding to the subframe can be obtained:
Er=L_Energy/T_Energy;
Comparing E r with a preset low-frequency energy power ratio E the, if E r>Ethe, the first wind noise identification flag wind_noise 1=1, and if E r<Ethe, wind_noise 1=0.
Step S202, based on the power spectrum difference algorithm, performing power spectrum analysis on the audio signal, and outputting a second wind noise identification.
In this step, since the fluctuation of the wind noise spectrum is small in the frequency range within 2kHz and the fluctuation of the human voice spectrum is large in the frequency range within 2kHz, the sum of the spectrum differences each within 2kHz can be calculated to identify the wind noise.
Optionally, based on the power spectrum difference algorithm, determining a power spectrum of each frequency point of the audio signal in a preset frequency range, calculating a power spectrum difference value of the audio signal in the preset frequency range according to the power spectrum of each frequency point, and determining a second wind noise identification identifier of the audio signal according to the power spectrum difference value.
In this alternative embodiment, the power spectrum difference is the sum of absolute differences between frequency-averaged power spectrums in adjacent frequency units of the audio signal, and is specifically as follows:
Simplifying to obtain:
wherein, the
Phi n is the sum of the absolute value differences of the average power spectrum, n represents the number of frames of the audio signal currently being processed, X n (l) represents the Fourier transform of the audio signal, K and l are the frequency points of the audio signal, xbar n (K) represents the average value of the absolute value of the power spectrum at the frequency point K,Is the difference between the average power spectrum absolute values of the M frequency points between the frequency point K and the frequency point K-1.
Since the power spectrum difference of the voice is larger than that of the wind voice and is obvious in a preset frequency range (such as below 2 Khz), the average power spectrum absolute value difference can be accumulated in the range of the first N frequency points to obtain phi n. If Φ n is greater than the preset power spectrum difference value θ Φ, the frame is determined to be speech, that is, the second wind noise identification flag wind_noise 2=0, otherwise, wind noise is determined to be wind noise, that is, wind_noise 2=1.
And step S203, performing LPC analysis on the audio signal based on the LPC analysis algorithm, and outputting a third wind noise identification.
In this step, the positions of the resonance points of the LPC analysis of different orders of wind noise are approximately the same, but the difference of the resonance points of the LPC analysis of different orders of human voice is large, that is, the wind noise under the LPC analysis of different orders has the same resonance peak, and the human voice has different resonance peaks, so that the wind noise can be identified by utilizing the characteristic.
Optionally, a second-order LPC analysis resonance peak point of the audio signal is determined based on the LPC analysis algorithm, the second-order LPC analysis resonance peak point is input into a preset LPC analysis polynomial to obtain a polynomial value, and a third wind noise identification mark of the audio signal is determined according to the polynomial value.
In this alternative embodiment, the LPC analysis algorithm is a speech signal linear prediction technique that uses existing old signals to predict new signals by polynomial fitting. The second-order LPC analysis expression is A (Z) =1+a 1z-1+a2z-2, Z=e++jw, the polynomial is the LPC expression in the Laplace transform domain, w is the angular frequency, j is the imaginary number, a is the LPC analysis coefficient, and the solution obtained when the polynomial is zero, namely the formant frequency point Z 0:
The second-order resonance peak point z 0 is led into LPC analysis polynomials with different orders, and the polynomials are close to zero when the second-order resonance peak point z 0 is a wind noise signal, namely, a third wind noise identification mark wind_noise 3=1, and if the second-order resonance peak point z 0 is not close to zero, the wind_noise 3=0.
In an embodiment, fig. 3 is a schematic flow chart of a method for reducing wind noise according to another embodiment of the present application. As shown in fig. 3, the step S102 specifically includes steps S301 to S303. It will be appreciated that the same steps as in fig. 1 are not repeated here.
Step S301, based on a plurality of wind noise identification identifiers, identifying an audio class of the audio signal, where the audio class includes pure human voice, pure wind noise and wind noise-containing human voice.
In this step, the wind noise identification mark includes a first wind noise identification mark obtained based on the analysis of the low-frequency power ratio algorithm, a second wind noise identification mark obtained based on the analysis of the power spectrum difference algorithm, and a third wind noise identification mark obtained based on the analysis of the LPC analysis algorithm.
Optionally, if the first wind noise identification identifier is not a first preset identifier, the second wind noise identification identifier is not a second preset identifier and the third wind noise identification identifier is not a third preset identifier, the audio class of the audio signal is judged to be pure human voice, if the first wind noise identification identifier is the first preset identifier, the second wind noise identification identifier is the second preset identifier or the third wind noise identification identifier is the third preset identifier, the audio class of the audio signal is judged to be wind noise-containing human voice, and if the first wind noise identification identifier is the first preset identifier, the second wind noise identification identifier is the second preset identifier and the third wind noise identification identifier is the third preset identifier, and the high-frequency energy ratio of the audio signal is smaller than a preset energy ratio, the audio class of the audio signal is judged to be pure wind noise.
In this alternative embodiment, the audio class is determined to be pure human voice if the wind_noise1, the wind_noise2 or the wind_noise3 are all 0, the wind noise is determined to be wind noise if the wind_noise1, the wind_noise2 or the wind_noise3 are all 1, and the high frequency energy ratio of the audio signal is smaller than the preset energy ratio if the wind_noise1, the wind_noise2 and the wind_noise3 are all 1.
Step S302, if the audio class of the audio signal is not pure wind noise or wind noise-containing human voice, determining whether a wind noise identification delay value is greater than a preset threshold value, wherein the wind noise identification delay value is used for representing whether the wind noise class of the last audio signal continuous with the audio signal is pure wind noise or wind noise-containing human voice.
In the step, in order to avoid carrying out error processing on pure voice, voice quality can be reserved for voice containing wind noise, so that recognition delay logic is introduced to compensate for missed detection of wind noise. Since wind noise that can affect call quality must be continuous, continuous wind noise is identified by detecting a significant wind onset and then using identification delay logic. Meanwhile, wind noise identification delay values (flag 1 and flag 2) dynamically change, so that false detection of non-wind noise sections can be avoided in time. Wherein, the flag1 is the wind noise identification delay value of the wind noise-containing human voice, and is considered to be the wind noise-containing human voice in the stage of which the value is not zero, the flag2 is the wind noise identification delay value of the pure wind noise, and is considered to be the pure wind noise in the stage of which the value is not zero.
Optionally, if the audio class of the audio signal is pure wind noise or wind noise-containing human voice, adding the wind noise identification delay value to a preset value to obtain a latest wind noise identification delay value, so as to be used for wind noise delay determination of a next audio signal continuous with the current audio signal. Wherein, for pure wind noise, the flag2+flag2 add=flag 1, the flag2add is a second preset value, and for wind noise-containing human noise, the flag1+flag1 add=flag 1, the flag1add is a first preset value.
Step S303, if the wind noise identification delay value is greater than a preset threshold value, determining that the wind noise type of the audio signal is pure wind noise or wind noise-containing human voice.
In this step, the preset threshold may be 0. For example, for the wind noise class of the previous audio signal continuous with the current audio signal being pure wind noise, it is determined whether the flag2 is greater than 0, and if the flag2>0, it is determined that the wind noise class of the current audio signal is pure wind noise, and meanwhile, the flag 2=the flag2-flag2add is updated. For the wind noise class of the last audio signal continuous with the current audio signal is wind noise-containing human voice, judging whether the flag1 is larger than 0, if the flag1 is larger than 0, judging that the wind noise class of the current audio signal is wind noise-containing human voice, and meanwhile, the flag 1=flag 1-flag1add to update the flag1.
Optionally, if the wind noise identification delay value is not greater than a preset threshold, setting the wind noise identification delay value as the preset threshold, and obtaining the latest wind noise identification delay value.
In this alternative embodiment, if the wind noise class of the previous audio signal continuous with the current audio signal is pure wind noise, it is determined whether the flag2 is greater than 0, if the flag2 is less than or equal to 0, it is determined that the current audio signal has no wind noise, and meanwhile, the flag 2=0, so as to update the flag2. For the wind noise type of the last audio signal continuous with the current audio signal, judging whether the flag1 is larger than 0 or not, if the flag1 is smaller than or equal to 0, judging that the current audio signal has no wind noise, and meanwhile, the flag 1=0 so as to update the flag1.
In an embodiment, fig. 4 is a schematic flow chart of a method for reducing wind noise according to still another embodiment of the present application. As shown in fig. 4, the step S103 includes a step S401 and a step S402. It will be appreciated that the same steps as in fig. 1 are not repeated here.
Step S401, if the wind noise class of the audio signal is pure wind noise, determining that the target wind noise filter is a single wind noise filter, where the single wind noise filter is used to perform wind noise filtering on the low frequency band of the audio signal.
In this step, because the traditional LPC filter is not well controlled to the filtering degree of wind noise, can't be with wind noise from the complete filtering of voice, simultaneously because the inaccurate quality that can influence of filtering, so simplify the improvement to the LPC filter of second order, let it focus on handling low frequency wind noise.
Optionally, performing euler transformation on a Laplacian domain of a preset LPC filter to obtain the single wind noise filter.
Wherein the preset LPC filter isK represents the order of LPC analysis, in this embodiment, K=2, Z is the Laplace domain, Z=e -jw, w is the angular frequency, j is the imaginary number, euler transformation is performed on the Laplace domain, that is, e jw =cos (x) +j.sin (x), and by trigonometric function simplification, operations such as taking only real numbers are performed first, so that a single wind noise filter is obtained:
L=1+a1×α×cos(w)+a2×α2×(2cos(w)cos(w)-1);
M=a1×α×sin(w)+a2×α2×(2sin(w)cos(w));
N=1+a1×β×cos(w)+a2×β2×(2cos(w)cos(w)-1);
R=a1×β×sin(w)+a2×β2×(2sin(w)cos(w));
α=0.3+w1×(0.6-0.3);
β=0.6+w2×(0.9-0.6);
Where w 1、w2 and μ are the attenuation coefficient.
The LPC filter is optimized, so that the calculation amount and the memory occupation of the single wind noise filter are reduced.
Step S402, if the wind noise class of the audio signal is wind noise, determining that the target wind noise filter is a combined wind noise filter, wherein the combined wind noise filter is used for carrying out wind noise filtering with different attenuation degrees on each frequency band of the audio signal.
In the step, in order to ensure the quality of high-frequency voice, low-frequency wind noise is filtered, a combination filter is designed by adopting an improved LPC filter, noise reduction filtering with different attenuation degrees is carried out in different frequency bands, namely, the combination wind noise filter is adopted for carrying out frequency division attenuation. Optionally, the single wind noise filter is adopted, and the attenuation degree coefficient is modified to reconstruct the corresponding combined wind noise filter, so that the operation amount and the memory occupation are reduced, the low-frequency wind noise is filtered more, the attenuation to high-frequency energy is small, and the human voice quality can be ensured.
In order to execute the wind noise reduction method corresponding to the method embodiment, corresponding functions and technical effects are realized. Referring to fig. 5, fig. 5 shows a block diagram of a wind noise reduction device according to an embodiment of the present application. For convenience of explanation, only the portions related to this embodiment are shown, and the wind noise reduction device provided by the embodiment of the present application includes:
the analysis module 501 is configured to perform signal analysis on an audio signal acquired by a microphone based on a plurality of preset signal analysis algorithms, and output a plurality of wind noise identification identifiers, where the wind noise identification identifiers are used to represent wind noise identification results corresponding to each preset signal analysis algorithm, and the microphone includes a single microphone;
a first determining module 502, configured to determine a wind noise class of the audio signal based on a plurality of wind noise identification identifiers, where the wind noise class includes pure wind noise and wind noise-containing human voice;
A second determining module 503, configured to determine, based on a wind noise class of the audio signal, a target wind noise filter corresponding to the wind noise class;
And the noise reduction module 504 is configured to reduce noise of the audio signal by using the target wind noise filter, and output a noise-reduced target audio signal.
In one embodiment, the analysis module 501 includes:
the first analysis unit is used for carrying out low-frequency power ratio analysis on the audio signal based on the low-frequency power ratio algorithm and outputting a first wind noise identification mark;
The second analysis unit is used for carrying out power spectrum analysis on the audio signal based on the power spectrum difference algorithm and outputting a second wind noise identification mark;
and the third analysis unit is used for performing LPC analysis on the audio signal based on the LPC analysis algorithm and outputting a third wind noise identification mark.
In an alternative embodiment, the first analysis unit includes:
a first calculation subunit for calculating low-frequency energy and full-band energy of the audio signal based on the low-frequency power ratio algorithm;
a second calculating subunit, configured to calculate a low-frequency energy power ratio of the audio signal according to the low-frequency energy and the full-band energy;
And the first determination subunit is used for determining a first wind noise identification mark of the audio signal according to the low-frequency energy power ratio.
In an alternative embodiment, the second analysis unit comprises:
The second determining subunit is used for determining the power spectrum of each frequency point of the audio signal in a preset frequency range based on the power spectrum difference algorithm;
A third calculation subunit, configured to calculate, according to the power spectrum of each frequency point, a power spectrum difference value of the audio signal in the preset frequency range;
and the third determination subunit is used for determining a second wind noise identification mark of the audio signal according to the power spectrum difference value.
In an alternative embodiment, the third analysis unit comprises:
A fourth determination subunit, configured to determine a second-order LPC analysis resonance peak point of the audio signal based on the LPC analysis algorithm;
the input subunit is used for inputting the second-order LPC analysis resonance peak points into a preset LPC analysis polynomial to obtain a polynomial value;
and a fifth determining subunit, configured to determine a third wind noise identification identifier of the audio signal according to the polynomial value.
In one embodiment, the first determining module 502 includes:
The identification unit is used for identifying the audio category of the audio signal based on a plurality of wind noise identification marks, wherein the audio category comprises pure human voice, pure wind noise and wind noise-containing human voice;
The first determining unit is used for determining whether the wind noise identification delay value is larger than a preset threshold value if the audio class of the audio signal is not pure wind noise or wind noise-containing human voice, and the wind noise identification delay value is used for representing whether the wind noise class of the last audio signal continuous with the audio signal is pure wind noise or wind noise-containing human voice;
And the judging unit is used for judging that the wind noise type of the audio signal is pure wind noise or wind noise-containing human voice if the wind noise identification delay value is larger than a preset threshold value.
In an optional embodiment, the wind noise identification identifier includes a first wind noise identification identifier obtained based on the analysis of the low-frequency power ratio algorithm, a second wind noise identification identifier obtained based on the analysis of the power spectrum difference algorithm, and a third wind noise identification identifier obtained based on the analysis of the LPC analysis algorithm, and the identification unit includes:
The first judging subunit is used for judging that the audio category of the audio signal is pure voice if the first wind noise identification mark is not a first preset mark, the second wind noise identification mark is not a second preset mark and the third wind noise identification mark is not a third preset mark;
A second judging subunit, configured to judge that an audio class of the audio signal is a voice containing wind noise if the first wind noise identification identifier is the first preset identifier, the second wind noise identification identifier is the second preset identifier, or the third wind noise identification identifier is the third preset identifier;
And the third judging subunit is used for judging that the audio category of the audio signal is pure wind noise if the first wind noise identification mark is the first preset mark, the second wind noise identification mark is the second preset mark and the third wind noise identification mark is the third preset mark and the high-frequency energy ratio of the audio signal is smaller than the preset energy ratio.
In an alternative embodiment, the first determining module 502 further includes:
and the adding unit is used for adding the wind noise identification delay value with a preset value to obtain the latest wind noise identification delay value if the audio class of the audio signal is pure wind noise or wind noise-containing human voice.
In an alternative embodiment, the first determining module 502 further includes:
And the setting unit is used for setting the wind noise identification delay value as the preset threshold value to obtain the latest wind noise identification delay value if the wind noise identification delay value is not larger than the preset threshold value.
In an embodiment, the second determining module 503 includes:
The second determining unit is used for determining that the target wind noise filter is a single wind noise filter if the wind noise class of the audio signal is pure wind noise, and the single wind noise filter is used for wind noise filtering of a low frequency band of the audio signal;
and the third determining unit is used for determining the target wind noise filter as a combined wind noise filter if the wind noise class of the audio signal is wind noise-containing human voice, and the combined wind noise filter is used for carrying out wind noise filtering with different attenuation degrees on each frequency band of the audio signal.
In an alternative embodiment, the second determining module 503 further includes:
and the transformation unit is used for carrying out Euler transformation on the Laplacian domain of the preset LPC filter to obtain the single wind noise filter.
The wind noise reduction device can implement the wind noise reduction method of the method embodiment. The options in the method embodiments described above are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the content of the above method embodiments, and in this embodiment, no further description is given.
Fig. 6 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 6, the terminal device 6 of this embodiment comprises at least one processor 60 (only one is shown in fig. 6), a memory 61 and a computer program 62 stored in said memory 61 and executable on said at least one processor 60, said processor 60 implementing the steps of any of the method embodiments described above when said computer program 62 is executed.
The terminal device 6 may be a smart phone, a tablet computer, a notebook computer, an earphone, and the like. The terminal device may include, but is not limited to, a processor 60, a memory 61. It will be appreciated by those skilled in the art that fig. 6 is merely an example of the terminal device 6 and is not meant to be limiting as to the terminal device 6, and may include more or fewer components than shown, or may combine certain components, or different components, such as may also include input-output devices, network access devices, etc.
The Processor 60 may be a central processing unit (Central Processing Unit, CPU), the Processor 60 may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL processors, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may in some embodiments be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may in other embodiments also be an external storage device of the terminal device 6, such as a plug-in hard disk provided on the terminal device 6, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing an operating system, application programs, boot loader (BootLoader), data, other programs, etc., such as program codes of the computer program. The memory 61 may also be used for temporarily storing data that has been output or is to be output.
In addition, the embodiment of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement the steps in any of the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product which, when run on a terminal device, causes the terminal device to perform the steps of the method embodiments described above.
In several embodiments provided by the present application, it will be understood that each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in the form of a software product stored in a storage medium comprising several instructions for causing a terminal device to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not to be construed as limiting the scope of the application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present application are intended to be included in the scope of the present application.

Claims (13)

1.一种降风噪方法，其特征在于，包括：1. A method for reducing wind noise, comprising: 基于多种预设信号分析算法，对麦克风采集的音频信号进行信号分析，输出多个风噪识别标识，所述风噪识别标识用于表示每种所述预设信号分析算法对应的风噪识别结果，所述麦克风包括单麦克风；Based on multiple preset signal analysis algorithms, signal analysis is performed on the audio signal collected by the microphone, and multiple wind noise identification marks are output, where the wind noise identification marks are used to indicate the wind noise identification results corresponding to each of the preset signal analysis algorithms, and the microphone includes a single microphone; 基于多个所述风噪识别标识，确定所述音频信号的风噪类别，所述风噪类别包括纯风噪和含风噪人声；其中，所述基于多个所述风噪识别标识，确定所述音频信号的风噪类别，包括：基于多个所述风噪识别标识，识别所述音频信号的音频类别，所述音频类别包括纯人声、纯风噪和含风噪人声；若所述音频信号的音频类别不为纯风噪或含风噪人声，则确定风噪识别延迟值是否大于预设阈值，所述风噪识别延迟值用于表征与所述音频信号连续的上一音频信号的风噪类别是否为纯风噪或含风噪人声；若所述风噪识别延迟值大于预设阈值，则判定所述音频信号的风噪类别为与上一音频信号相同的风噪类别；Based on a plurality of the wind noise identification identifiers, determining the wind noise category of the audio signal, the wind noise category including pure wind noise and human voice containing wind noise; wherein, determining the wind noise category of the audio signal based on a plurality of the wind noise identification identifiers comprises: based on a plurality of the wind noise identification identifiers, identifying the audio category of the audio signal, the audio category including pure human voice, pure wind noise and human voice containing wind noise; if the audio category of the audio signal is not pure wind noise or human voice containing wind noise, determining whether a wind noise identification delay value is greater than a preset threshold, the wind noise identification delay value being used to characterize whether the wind noise category of the previous audio signal continuous with the audio signal is pure wind noise or human voice containing wind noise; if the wind noise identification delay value is greater than the preset threshold, determining that the wind noise category of the audio signal is the same wind noise category as the previous audio signal; 基于所述音频信号的风噪类别，确定与所述风噪类别对应的目标风噪滤波器；Based on the wind noise category of the audio signal, determining a target wind noise filter corresponding to the wind noise category; 利用所述目标风噪滤波器，对音频信号进行降噪，输出降噪后的目标音频信号。The target wind noise filter is used to reduce the noise of the audio signal, and the target audio signal after the noise reduction is output. 2.如权利要求1所述的降风噪方法，其特征在于，所述预设信号分析算法包括低频功率比算法、功率谱差异算法和LPC分析算法，所述基于多种预设信号分析算法，对麦克风采集的音频信号进行信号分析，输出多个风噪识别标识，包括：2. The wind noise reduction method according to claim 1, characterized in that the preset signal analysis algorithm includes a low frequency power ratio algorithm, a power spectrum difference algorithm and an LPC analysis algorithm, and the audio signal collected by the microphone is analyzed based on multiple preset signal analysis algorithms to output multiple wind noise identification marks, including: 基于所述低频功率比算法，对所述音频信号进行低频功率比分析，输出第一风噪识别标识；Based on the low-frequency power ratio algorithm, perform low-frequency power ratio analysis on the audio signal and output a first wind noise identification mark; 基于所述功率谱差异算法，对所述音频信号进行功率谱分析，输出第二风噪识别标识；Based on the power spectrum difference algorithm, performing power spectrum analysis on the audio signal, and outputting a second wind noise identification mark; 基于所述LPC分析算法，对所述音频信号进行LPC分析，输出第三风噪识别标识。Based on the LPC analysis algorithm, LPC analysis is performed on the audio signal, and a third wind noise identification mark is output. 3.如权利要求2所述的降风噪方法，其特征在于，所述基于所述低频功率比算法，对所述音频信号进行低频功率比分析，输出第一风噪识别标识，包括：3. The wind noise reduction method according to claim 2, wherein the step of performing low-frequency power ratio analysis on the audio signal based on the low-frequency power ratio algorithm and outputting a first wind noise identification mark comprises: 基于所述低频功率比算法，计算所述音频信号的低频能量和全频带能量；Based on the low frequency power ratio algorithm, calculating the low frequency energy and full frequency band energy of the audio signal; 根据所述低频能量和全频带能量，计算所述音频信号的低频能量功率比；Calculating a low-frequency energy power ratio of the audio signal according to the low-frequency energy and the full-band energy; 根据所述低频能量功率比，确定所述音频信号的第一风噪识别标识。A first wind noise identification identifier of the audio signal is determined according to the low-frequency energy power ratio. 4.如权利要求2所述的降风噪方法，其特征在于，所述基于所述功率谱差异算法，对所述音频信号进行功率谱分析，输出第二风噪识别标识，包括：4. The wind noise reduction method according to claim 2, wherein the step of performing power spectrum analysis on the audio signal based on the power spectrum difference algorithm and outputting a second wind noise identification mark comprises: 基于所述功率谱差异算法，确定所述音频信号在预设频率范围内各个频率点的功率谱；Based on the power spectrum difference algorithm, determining the power spectrum of each frequency point of the audio signal within a preset frequency range; 根据各个频率点的功率谱，计算所述音频信号在所述预设频率范围内的功率谱差值；Calculating the power spectrum difference of the audio signal within the preset frequency range according to the power spectrum of each frequency point; 根据所述功率谱差值，确定所述音频信号的第二风噪识别标识。A second wind noise identification identifier of the audio signal is determined according to the power spectrum difference. 5.如权利要求2所述的降风噪方法，其特征在于，所述基于所述LPC分析算法，对所述音频信号进行LPC分析，输出第三风噪识别标识，包括：5. The wind noise reduction method according to claim 2, wherein the step of performing LPC analysis on the audio signal based on the LPC analysis algorithm and outputting a third wind noise identification mark comprises: 基于所述LPC分析算法，确定所述音频信号的二阶LPC分析共振峰点；Based on the LPC analysis algorithm, determining the second-order LPC analysis resonance peak point of the audio signal; 将所述二阶LPC分析共振峰点输入到预设的LPC分析多项式，得到多项式值；Inputting the second-order LPC analysis resonance peak point into a preset LPC analysis polynomial to obtain a polynomial value; 根据所述多项式值，确定所述音频信号的第三风噪识别标识。A third wind noise identification identifier of the audio signal is determined according to the polynomial value. 6.如权利要求1所述的降风噪方法，其特征在于，所述风噪识别标识包括基于低频功率比算法分析得到的第一风噪识别标识、基于功率谱差异算法分析得到第二风噪识别标识以及基于LPC分析算法分析得到的第三风噪识别标识，所述基于多个所述风噪识别标识，识别所述音频信号的音频类别，包括：6. The wind noise reduction method according to claim 1, characterized in that the wind noise identification identifier comprises a first wind noise identification identifier obtained based on a low-frequency power ratio algorithm analysis, a second wind noise identification identifier obtained based on a power spectrum difference algorithm analysis, and a third wind noise identification identifier obtained based on an LPC analysis algorithm analysis, and the identifying the audio category of the audio signal based on the plurality of wind noise identification identifiers comprises: 若所述第一风噪识别标识不为第一预设标识、所述第二风噪识别标识不为第二预设标识和所述第三风噪识别标识不为第三预设标识，则判定所述音频信号的音频类别为纯人声；If the first wind noise identification mark is not a first preset mark, the second wind noise identification mark is not a second preset mark, and the third wind noise identification mark is not a third preset mark, determining that the audio category of the audio signal is pure human voice; 若所述第一风噪识别标识为所述第一预设标识、所述第二风噪识别标识为所述第二预设标识或所述第三风噪识别标识为所述第三预设标识，则判定所述音频信号的音频类别为含风噪人声；If the first wind noise identification identifier is the first preset identifier, the second wind noise identification identifier is the second preset identifier, or the third wind noise identification identifier is the third preset identifier, determining that the audio category of the audio signal is a human voice containing wind noise; 若所述第一风噪识别标识为所述第一预设标识、所述第二风噪识别标识为所述第二预设标识和所述第三风噪识别标识为所述第三预设标识，且所述音频信号的高频能量比小于预设能量比，则判定所述音频信号的音频类别为纯风噪。If the first wind noise identification identifier is the first preset identifier, the second wind noise identification identifier is the second preset identifier, and the third wind noise identification identifier is the third preset identifier, and the high-frequency energy ratio of the audio signal is less than the preset energy ratio, the audio category of the audio signal is determined to be pure wind noise. 7.如权利要求1所述的降风噪方法，其特征在于，所述基于多个所述风噪识别标识，识别所述音频信号的音频类别之后，还包括：7. The wind noise reduction method according to claim 1, characterized in that after the audio category of the audio signal is identified based on the plurality of wind noise identification identifiers, the method further comprises: 若所述音频信号的音频类别为纯风噪或含风噪人声，则将所述风噪识别延迟值与预设值相加，得到最新的风噪识别延迟值。If the audio category of the audio signal is pure wind noise or human voice containing wind noise, the wind noise recognition delay value is added to a preset value to obtain the latest wind noise recognition delay value. 8.如权利要求1所述的降风噪方法，其特征在于，所述若所述音频信号的音频类别不为纯风噪或含风噪人声，则确定风噪识别延迟值是否大于预设阈值之后，还包括：8. The wind noise reduction method according to claim 1, characterized in that if the audio category of the audio signal is not pure wind noise or human voice containing wind noise, after determining whether the wind noise recognition delay value is greater than a preset threshold, it also includes: 若所述风噪识别延迟值不大于预设阈值，则将所述风噪识别延迟值设置为所述预设阈值，得到最新的风噪识别延迟值。If the wind noise recognition delay value is not greater than the preset threshold, the wind noise recognition delay value is set to the preset threshold to obtain the latest wind noise recognition delay value. 9.如权利要求1所述的降风噪方法，其特征在于，所述基于所述音频信号的风噪类别，确定与所述风噪类别对应的目标风噪滤波器，包括：9. The wind noise reduction method according to claim 1, wherein determining a target wind noise filter corresponding to the wind noise category based on the wind noise category of the audio signal comprises: 若所述音频信号的风噪类别为纯风噪，则确定所述目标风噪滤波器为单风噪滤波器，所述单风噪滤波器用于对所述音频信号的低频段进行风噪滤波；If the wind noise category of the audio signal is pure wind noise, determining that the target wind noise filter is a single wind noise filter, the single wind noise filter is used to perform wind noise filtering on a low frequency band of the audio signal; 若所述音频信号的风噪类别为含风噪人声，则确定所述目标风噪滤波器为组合风噪滤波器，所述组合风噪滤波器用于对所述音频信号的各个频段进行不同衰减程度的风噪滤波。If the wind noise category of the audio signal is human voice containing wind noise, the target wind noise filter is determined to be a combined wind noise filter, and the combined wind noise filter is used to perform wind noise filtering with different attenuation degrees on each frequency band of the audio signal. 10.如权利要求9所述的降风噪方法，其特征在于，所述若所述音频信号的风噪类别为纯风噪，则确定所述目标风噪滤波器为单风噪滤波器之前，还包括：10. The wind noise reduction method according to claim 9, characterized in that if the wind noise category of the audio signal is pure wind noise, before determining that the target wind noise filter is a single wind noise filter, the method further comprises: 对预设LPC滤波器的拉普拉斯域进行欧拉变换，得到所述单风噪滤波器。The single wind noise filter is obtained by performing an Euler transform on the Laplace domain of the preset LPC filter. 11.一种降风噪装置，其特征在于，包括：11. A wind noise reduction device, comprising: 分析模块，用于基于多种预设信号分析算法，对麦克风采集的音频信号进行信号分析，输出多个风噪识别标识，所述风噪识别标识用于表示每种所述预设信号分析算法对应的风噪识别结果，所述麦克风包括单麦克风；An analysis module, configured to perform signal analysis on the audio signal collected by the microphone based on a plurality of preset signal analysis algorithms, and output a plurality of wind noise identification marks, wherein the wind noise identification marks are used to indicate the wind noise identification result corresponding to each of the preset signal analysis algorithms, wherein the microphone includes a single microphone; 第一确定模块，用于基于多个所述风噪识别标识，确定所述音频信号的风噪类别，所述风噪类别包括纯风噪和含风噪人声；其中，所述基于多个所述风噪识别标识，确定所述音频信号的风噪类别，包括：基于多个所述风噪识别标识，识别所述音频信号的音频类别，所述音频类别包括纯人声、纯风噪和含风噪人声；若所述音频信号的音频类别不为纯风噪或含风噪人声，则确定风噪识别延迟值是否大于预设阈值，所述风噪识别延迟值用于表征与所述音频信号连续的上一音频信号的风噪类别是否为纯风噪或含风噪人声；若所述风噪识别延迟值大于预设阈值，则判定所述音频信号的风噪类别为与上一音频信号相同的风噪类别；A first determination module is used to determine the wind noise category of the audio signal based on a plurality of wind noise identification identifiers, the wind noise category including pure wind noise and human voice containing wind noise; wherein, determining the wind noise category of the audio signal based on a plurality of wind noise identification identifiers comprises: identifying the audio category of the audio signal based on a plurality of wind noise identification identifiers, the audio category including pure human voice, pure wind noise and human voice containing wind noise; if the audio category of the audio signal is not pure wind noise or human voice containing wind noise, determining whether a wind noise identification delay value is greater than a preset threshold, the wind noise identification delay value being used to characterize whether the wind noise category of the previous audio signal continuous with the audio signal is pure wind noise or human voice containing wind noise; if the wind noise identification delay value is greater than the preset threshold, determining that the wind noise category of the audio signal is the same wind noise category as the previous audio signal; 第二确定模块，用于基于所述音频信号的风噪类别，确定与所述风噪类别对应的目标风噪滤波器；A second determination module, configured to determine, based on the wind noise category of the audio signal, a target wind noise filter corresponding to the wind noise category; 降噪模块，用于利用所述目标风噪滤波器，对音频信号进行降噪，输出降噪后的目标音频信号。The noise reduction module is used to use the target wind noise filter to reduce the noise of the audio signal and output the target audio signal after the noise reduction. 12.一种终端设备，其特征在于，包括处理器和存储器，所述存储器用于存储计算机程序，所述计算机程序被所述处理器执行时实现如权利要求1至10任一项所述的降风噪方法的步骤。12. A terminal device, characterized in that it comprises a processor and a memory, wherein the memory is used to store a computer program, and when the computer program is executed by the processor, the steps of the wind noise reduction method according to any one of claims 1 to 10 are implemented. 13.一种计算机可读存储介质，其特征在于，其存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至10任一项所述的降风噪方法的步骤。13. A computer-readable storage medium, characterized in that it stores a computer program, and when the computer program is executed by a processor, the steps of the wind noise reduction method according to any one of claims 1 to 10 are implemented.
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