1. Reid Draper
  2. acousticfeatures

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Welcome to the source page for my University of Miami MMI 505 project. With millions of sound recordings now available at the click of a mouse, the ability to find music in useful ways that go beyond a traditional textual metadata search, is becoming increasingly important and viable. My project was to write a Python module that extracts low-level acoustic features from an audio file (.wav or .mp3) on a frame-by-frame basis and computes meta-features based on these vectors.

Use like this:

from acousticfeatures import *
x = FeatureExtractor('White_Noise.wav') #can read .wav or .mp3
x.process_frames()
feature_dict = x.return_features()

feature_dict now contains a dictionary with entries for each of the features. The implemented features are: Mean and Standard deviation for these time vectors: RMS, Spectrum Centroid, Spectrum Spread, Spectrum Skewness, Spectrum Kurtosis and Zero Crossings.

$ hg clone http://bitbucket.org/reiddraper/acousticfeatures/wiki/

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