- This algorithm is based (but not completely reproducing) on the one outlined by Audacity for the noise reduction effect (Link to C++ code)
- The algorithm takes two inputs:
- A noise audio clip containing prototypical noise of the audio clip (optional)
- A signal audio clip containing the signal and the noise intended to be removed
- An FFT is calculated over the noise audio clip
- Statistics are calculated over FFT of the the noise (in frequency)
- A threshold is calculated based upon the statistics of the noise (and the desired sensitivity of the algorithm)
- An FFT is calculated over the signal
- A mask is determined by comparing the signal FFT to the threshold
- The mask is smoothed with a filter over frequency and time
- The mask is appled to the FFT of the signal, and is inverted
pip install noisereduce
noisereduce optionally uses Tensorflow as a backend to speed up FFT and gaussian convolution. It is not listed in the requirements.txt so because (1) it is optional and (2) tensorflow-gpu and tensorflow (cpu) are both compatible with this package. The package requires Tensorflow 2+ for all tensorflow operations.
See example notebook:
import noisereduce as nr
# load data
rate, data = wavfile.read("mywav.wav")
# select section of data that is noise
noisy_part = data[10000:15000]
# perform noise reduction
reduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noisy_part, verbose=True)
n_grad_freq (int): how many frequency channels to smooth over with the mask.
n_grad_time (int): how many time channels to smooth over with the mask.
n_fft (int): number audio of frames between STFT columns.
win_length (int): Each frame of audio is windowed by `window()`. The window will be of length `win_length` and then padded with zeros to match `n_fft`..
hop_length (int):number audio of frames between STFT columns.
n_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal
prop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none)
pad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data
use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation
verbose (bool): Whether to plot the steps of the algorithm
If you use this code in your research, please cite it:
@software{tim_sainburg_2019_3243139,
author = {Tim Sainburg},
title = {timsainb/noisereduce: v1.0},
month = jun,
year = 2019,
publisher = {Zenodo},
version = {db94fe2},
doi = {10.5281/zenodo.3243139},
url = {https://doi.org/10.5281/zenodo.3243139}
}
or
@article{sainburg2020finding,
title={Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires},
author={Sainburg, Tim and Thielk, Marvin and Gentner, Timothy Q},
journal={PLoS computational biology},
volume={16},
number={10},
pages={e1008228},
year={2020},
publisher={Public Library of Science}
}
Project based on the cookiecutter data science project template. #cookiecutterdatascience
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