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whatsound - a Python ML toolkit for audio classification

whatsound is a toolkit for training, and testing audio classification using a neural network.

  • Music
  • Speech
  • Ambient/Noise
  • Silence

This toolkit uses Essentia for audio feature extraction, and PyBrain for the use of a backpropagation neural network for the training and testing of classification.

How it works

WS_classify.py

This is the toolkit for classification, which exposes the main classification functionality.

Source modules

The project is split into modules fit for different purposes.

/core

These modules are needed for audio training and classification.

WS_extractor.py

Extracts audio features from a stream. The Essentia library is used for audio analysis. The features which are used for extraction are:

  • MFCC
  • Zero crossing rate
  • Key strength
  • Spectral Flux
  • Pitch strength
  • LPC

WS_utils.py

Utility functions

WS_global_data.py

These are global parameters - settings for the neural net, training parameters, audio settings and classifier types.

WS_network.py

This module allows training and testing of a data set, with optional the following parameters:

  • weights : the path to a PyBrain weights XML file
  • dataset: the path to a directory containing audio samples split by class
  • split: the ratio with which to split the data set between training/testing