Solo Voice Detection Via Optimal Cancellation
Christine E. Smit; Daniel P. W. Ellis
- Solo Voice Detection Via Optimal Cancellation
Smit, Christine E.
Ellis, Daniel P. W.
- Electrical Engineering
- Permanent URL:
- Book/Journal Title:
- 2007 Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), October 21-24, 2007, New Paltz, NY
- Publisher Location:
- Piscataway, N.J.
- 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.
- Electrical engineering
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