# noisereduce
**Repository Path**: saber5433/noisereduce
## Basic Information
- **Project Name**: noisereduce
- **Description**: Noise reduction / speech enhancement for python using spectral gating
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-04-09
- **Last Updated**: 2021-01-28
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://travis-ci.com/timsainb/noisereduce)
[](https://coveralls.io/github/timsainb/noisereduce?branch=master)
[](https://mybinder.org/v2/gh/timsainb/noisereduce/master?filepath=notebooks%2F1.0-test-noise-reduction.ipynb)
[](https://colab.research.google.com/github/timsainb/noisereduce/blob/master/notebooks/1.0-test-noise-reduction.ipynb)
[](https://badge.fury.io/py/noisereduce)
# Noise reduction in python using spectral gating
- This algorithm is based (but not completely reproducing) on the one [outlined by Audacity](https://wiki.audacityteam.org/wiki/How_Audacity_Noise_Reduction_Works) for the **noise reduction effect** ([Link to C++ code](https://github.com/audacity/audacity/blob/master/src/effects/NoiseReduction.cpp))
- The algorithm requires two inputs:
1. A *noise* audio clip comtaining prototypical noise of the audio clip
2. A *signal* audio clip containing the signal and the noise intended to be removed
## Steps of algorithm
1. An FFT is calculated over the noise audio clip
2. Statistics are calculated over FFT of the the noise (in frequency)
3. A threshold is calculated based upon the statistics of the noise (and the desired sensitivity of the algorithm)
4. An FFT is calculated over the signal
5. A mask is determined by comparing the signal FFT to the threshold
6. The mask is smoothed with a filter over frequency and time
7. The mask is appled to the FFT of the signal, and is inverted
## Installation
`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.*
## Usage
See example notebook: [](https://colab.research.google.com/github/timsainb/noisereduce/blob/master/notebooks/1.0-test-noise-reduction.ipynb)
```
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)
```
### Arguments to `noise_reduce`
```
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
```
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Project based on the cookiecutter data science project template. #cookiecutterdatascience
