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Project Description

Motivation

Zounds is a python library for working with sound. Its primary goals are to:

Audio processing graphs and machine learning pipelines are defined using featureflow.

A Quick Example

import zounds

Resampled = zounds.resampled(resample_to=zounds.SR11025())


@zounds.simple_in_memory_settings
class Sound(Resampled):
    """
    A simple pipeline that computes a perceptually weighted modified discrete
    cosine transform, and "persists" feature data in an in-memory store.
    """

    windowed = zounds.ArrayWithUnitsFeature(
        zounds.SlidingWindow,
        needs=Resampled.resampled,
        wscheme=zounds.HalfLapped(),
        wfunc=zounds.OggVorbisWindowingFunc(),
        store=True)

    mdct = zounds.ArrayWithUnitsFeature(
        zounds.MDCT,
        needs=windowed,
        store=False)

    weighted = zounds.ArrayWithUnitsFeature(
        zounds.FrequencyWeighting,
        weighting=zounds.AWeighting(),
        needs=mdct,
        store=False)

if __name__ == '__main__':

    # produce some audio to test our pipeline
    synth = zounds.SineSynthesizer(zounds.SR44100())
    samples = synth.synthesize(zounds.Seconds(5), [220., 440., 880.])

    # process the audio, and fetch features from our in-memory store
    _id = Sound.process(meta=samples.encode())
    sound = Sound(_id)

    # produce a time slice that starts half a second in, and lasts for two
    # seconds
    time_slice = zounds.TimeSlice(
        start=zounds.Milliseconds(500),
        duration=zounds.Seconds(2))
    # grab all the frequency information, for a subset of the duration
    snippet = sound.weighted[time_slice, :]

    # produce a frequency slice that spans 400hz-500hz
    freq_band = zounds.FrequencyBand(400, 500)
    # grab a subset of frequency information for the duration of the sound
    a440 = sound.mdct[:, freq_band]

    # produce a new set of coefficients where only the 440hz sine wave is
    # present
    filtered = sound.mdct.copy()
    filtered[:] = 0
    filtered[:, freq_band] = a440

    # apply a geometric scale, which more closely matches human pitch
    # perception, and apply it to the linear frequency axis
    scale = zounds.GeometricScale(50, 4000, 0.05, 100)
    bands = [sound.weighted[:, band] for band in scale]
    band_sizes = [band.shape[1] for band in bands]

    # reconstruct audio from the MDCT coefficients
    mdct_synth = zounds.MDCTSynthesizer()
    reconstructed = mdct_synth.synthesize(sound.mdct)
    filtered_reconstruction = mdct_synth.synthesize(filtered)

    # start an in-browser REPL that will allow you to listen to and visualize
    # the variables defined above (and any new ones you create in the session)
    app = zounds.ZoundsApp(
        model=Sound,
        audio_feature=Sound.ogg,
        visualization_feature=Sound.weighted,
        globals=globals(),
        locals=locals())
    app.start(8888)

Find more inspiration in the examples folder, or on the blog.

Installation

Libsndfile Issues

Installation currently requires you to build lbiflac and libsndfile from source, because of an outstanding issue that will be corrected when the apt package is updated to libsndfile 1.0.26. Download and run this script to handle this step.

Numpy and Scipy

The Anaconda python distribution is highly recommended.

Zounds

Finally, just:

pip install zounds
Release History

Release History

This version
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0.23.12

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0.21.12

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0.20.11

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0.1

Download Files

Download Files

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File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
zounds-0.23.12.tar.gz (166.2 kB) Copy SHA256 Checksum SHA256 Source Dec 13, 2017

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