Motivation

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

• layer semantically meaningful audio manipulations on top of numpy arrays
• help to organize the definition and persistence of audio processing pipelines and machine learning experiments with sound

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)

    weighted = zounds.ArrayWithUnitsFeature(
        lambda x: x * zounds.AWeighting(),
        needs=mdct)

if __name__ == '__main__':

    # produce some audio to test our pipeline, and encode it as FLAC
    synth = zounds.SineSynthesizer(zounds.SR44100())
    samples = synth.synthesize(zounds.Seconds(5), [220., 440., 880.])
    encoded = samples.encode(fmt='FLAC')

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

    # grab all the frequency information, for a subset of the duration
    start = zounds.Milliseconds(500)
    end = start + zounds.Seconds(2)
    snippet = sound.weighted[start: end, :]

    # grab a subset of frequency information for the duration of the sound
    freq_band = slice(zounds.Hertz(400), zounds.Hertz(500))
    a440 = sound.mdct[:, freq_band]

    # produce a new set of coefficients where only the 440hz sine wave is
    # present
    filtered = sound.mdct.zeros_like()
    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)
    log_coeffs = scale.apply(sound.mdct, zounds.HanningWindowingFunc())

    # 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(9999)

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