Pitch Based Sound Classification

Andreas Brinch Nielsen, Lars Kai Hansen, Ulrik Kjems

AbstractA sound classification model is presented that can classify
signals into music, noise and speech. The model extracts
the pitch of the signal using the harmonic product spectrum.
Based on the pitch estimate and a pitch error measure, features
are created and used in a probabilistic model with softmax
output function. Both linear and quadratic inputs are
used. The model is trained on 2 hours of sound and tested
on publically available data. A test classification error below
0.05 with 1 s classification windows is achieved. Further more
it is shown that linear input performs as well as a quadratic,
and that even though classification gets marginally better,
not much is achieved by increasing the window size beyond
1 s.
KeywordsSound classification, pitch, HPS
TypeConference paper [With referee]
ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Year2006    Month May
PublisherToulouse, France
Electronic version(s)[pdf]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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