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easy-to-use implementation of the ISMIR 2013 Audio Degradation Toolbox
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sevagh/audio-degradation-toolbox
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Python 3 implementation of the MATLABAudio Degradation Toolbox. The license is GPL due to the original code being GPL. The aim is full feature parity with:
- Original MATLAB toolbox (with ISMIR2013 additions)
- A similar tool,audio_degrader
This tool can read non-WAV files as input, but only outputs single-channel WAV files - this is because I find that WAV is the most universal format with friendly-licensed libraries in any language.
$ audio-degradation-toolbox -husage: audio-degradation-toolbox [-h] [-d DEGRADATIONS_FILE] [-p] [-t] input_path output_pathApply controlled degradations to an audio file, specified in a JSON file containing an array of degradations (executed in order).Paths are relative to the execution dir, and square brackets denote optional arguments along with their default values. { "name": "noise", ["snr": 20, "color": "pink"] } { "name": "mp3", ["bitrate": 320] } { "name": "gain", ["volume": 10.0] } { "name": "normalize" } { "name": "low_pass", ["cutoff": 1000.0] } { "name": "high_pass", ["cutoff": 1000.0] } { "name": "trim_millis", ["amount": 100, "offset": 0] } { "name": "mix", "path": STRING, ["snr": 20.0] } { "name": "speedup", "speed": FLOAT } { "name": "resample", "rate": INT } { "name": "pitch_shift", "octaves": FLOAT } { "name": "dynamic_range_compression", ["threshold": -20.0, "ratio": 4.0, "attack": 5.0, "release": 50.0] } { "name": "impulse_response", "path": STRING } { "name": "equalizer", "frequency": FLOAT, ["bandwidth": 1.0, "gain": -3.0] } { "name": "time_stretch", "factor": FLOAT } { "name": "delay", "n_samples": INT } { "name": "clipping", ["n_samples": 0, "percent_samples": 0.0] } { "name": "wow_flutter", ["intensity": 1.5, "frequency": 0.5, "upsampling_factor": 5.0 ] } { "name": "aliasing", ["dest_frequency": 8000.0] } { "name": "harmonic_distortion", ["num_passes": 3] }positional arguments: input_path Path to input file output_path Path to output WAV fileoptional arguments: -h, --help show this help message and exit -d DEGRADATIONS_FILE, --degradations-file DEGRADATIONS_FILE JSON file of degradations to apply -p, --play Play file audio at each degradation step -t, --trim Trim trailing and leading silencesSome of the ISMIR2013 degradations are chains of basic degradations. Given the JSON format that my tool accepts, these are most easily represented as JSON files in thepresets dir.
Samples, mostly IR wav files, come from the original MATLAB repository. I've resampled them from 96000 to 48000 with ffmpeg, since pydub has questionable support for 96000.
It should be as easy aspip3 install . after cloning this repository. Afterwards, runaudio-degradation-toolbox. You may need to installsox from your OS package manager for some effects.
To develop,pip3 install -e .. The code is formatted withblack, so run that before contributing anything. To run tests, runpython3 -m unittest discover.
To use the--play flag (i.e. play the audio clip between each degradation), you must havempv installed and in$PATH:
sevagh:audio-degradation-toolbox $ audio-degradation-toolbox \ Viola.arco.ff.sulC.E3.stereo.aiff \ Viola-E3-degraded.wav \ --degradations-file ./degradations.json \ --playPlaying audio before degradationsA: 00:00:03 / 00:00:03 (93%)Applied degradation noise with params color: white, snr: 20Playing audio after degradationA: 00:00:03 / 00:00:03 (93%)Write the desired degradations in a JSON file, e.g.:
$ cat degradations.json[ { "name": "trim_millis", "amount": 500 }, { "name": "noise", "color": "violet", "snr": 10 }, { "name": "mix", "path": "./restaurant08.wav", "snr": 15 }]Afterwards, apply the degradations with:
$ audio-degradation-toolbox -d degradations.json in.wav out_degraded.wavMfccMeanAdaption and AdaptiveEqualizer (both from the MATLAB original).