A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. Mix in another sound, e.g. a background noise. Useful if your original sound is clean and you want to simulate an environment where background noise is present. A folder of (background noise) sounds to be mixed in must be specified. These sounds should ideally be at least as long as the input sounds to be transformed. Otherwise, the background sound will be repeated, which may sound unnatural. Note that the gain of the added noise is relative to the amount of signal in the input. This implies that if the input is completely silent, no noise will be added.

Features

  • Mix in various (bursts of overlapping) sounds with random pauses between
  • Apply a Lowpass-like filterbank with variable octave attenuation that simulates attenuation of higher frequencies due to air absorption in some cases
  • Apply band-pass filtering to the input audio
  • Filter steepness (6/12/18... dB / octave) is parametrized
  • Apply band-stop filtering to the input audio. Also known as notch filter or band reject filter
  • Clip audio by specified values

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License

MIT License

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Additional Project Details

Programming Language

Python

Related Categories

Python Sound Audio, Python Machine Learning Software, Python Deep Learning Frameworks

Registered

2022-08-22