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            "Intended Audience :: Science/Research",
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            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Topic :: Education",
            "Topic :: Scientific/Engineering"
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        "description": "[![Build Status](https://travis-ci.com/timsainb/noisereduce.svg?branch=master)](https://travis-ci.com/timsainb/noisereduce)\n[![Coverage Status](https://coveralls.io/repos/github/timsainb/noisereduce/badge.svg?branch=master)](https://coveralls.io/github/timsainb/noisereduce?branch=master)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/timsainb/noisereduce/master?filepath=notebooks%2F1.0-test-noise-reduction.ipynb)\n[![PyPI version](https://badge.fury.io/py/noisereduce.svg)](https://badge.fury.io/py/noisereduce)\n[![DOI](https://zenodo.org/badge/174219582.svg)](https://zenodo.org/badge/latestdoi/174219582)\n\n\n# Noise reduction in python using spectral gating\n- 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))\n- The algorithm requires two inputs: \n    1. A *noise* audio clip comtaining prototypical noise of the audio clip\n    2. A *signal* audio clip containing the signal and the noise intended to be removed\n\n## Steps of algorithm\n1. An FFT is calculated over the noise audio clip\n2. Statistics are calculated over FFT of the the noise (in frequency)\n3. A threshold is calculated based upon the statistics of the noise (and the desired sensitivity of the algorithm) \n4. An FFT is calculated over the signal\n5. A mask is determined by comparing the signal FFT to the threshold\n6. The mask is smoothed with a filter over frequency and time\n7. The mask is appled to the FFT of the signal, and is inverted\n\n## Installation\n`pip install noisereduce`\n\n*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.* \n\n## Usage\n(see notebooks)\n\n```\nimport noisereduce as nr\n# load data\nrate, data = wavfile.read(\"mywav.wav\")\n# select section of data that is noise\nnoisy_part = data[10000:15000]\n# perform noise reduction\nreduced_noise = nr.reduce_noise(audio_clip=data, noise_clip=noisy_part, verbose=True)\n```\n\n### Arguments to `noise_reduce`\n```\nn_grad_freq (int): how many frequency channels to smooth over with the mask.\nn_grad_time (int): how many time channels to smooth over with the mask.\nn_fft (int): number audio of frames between STFT columns.\nwin_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`..\nhop_length (int):number audio of frames between STFT columns.\nn_std_thresh (int): how many standard deviations louder than the mean dB of the noise (at each frequency level) to be considered signal\nprop_decrease (float): To what extent should you decrease noise (1 = all, 0 = none)\npad_clipping (bool): Pad the signals with zeros to ensure that the reconstructed data is equal length to the data\n        use_tensorflow (bool): Use tensorflow as a backend for convolution and fft to speed up computation\nverbose (bool): Whether to plot the steps of the algorithm\n```\n<div style=\"text-align:center\">\n<p align=\"center\">\n  <img src=\"assets/noisereduce.png\", width=\"100%\">\n</p>\n</div>\n\n\n--------\n\n<p><small>Project based on the <a target=\"_blank\" href=\"https://drivendata.github.io/cookiecutter-data-science/\">cookiecutter data science project template</a>. #cookiecutterdatascience</small></p>\n\n\n",
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