No project description provided.
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, 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 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)
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.
pip install zounds