Solo Voice Detection Via Optimal Cancellation

Smit, Christine E.; Ellis, Daniel P. W.

Automatically identifying sections of solo voices or instruments within a large corpus of music recordings would be useful, for example, to construct a library of isolated instruments to train signal models. We consider several ways to identify these sections, including a baseline classifier trained on conventional speech features. Our best results, achieving frame level precision and recall of around 70%, come from an approach that attempts to track the local periodicity of an assumed solo musical voice, then classifies the segment as a genuine solo or not on the basis of what proportion of the energy can be canceled by a comb filter constructed to remove just that periodicity.


Also Published In

2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), October 21-24, 2007, New Paltz, NY

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Academic Units
Electrical Engineering
Published Here
June 27, 2012