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Solo Voice Detection Via Optimal Cancellation

Christine E. Smit; Daniel P. W. Ellis

Title:
Solo Voice Detection Via Optimal Cancellation
Author(s):
Smit, Christine E.
Ellis, Daniel P. W.
Date:
Type:
Articles
Department:
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:
IEEE
Publisher Location:
Piscataway, N.J.
Abstract:
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.
Subject(s):
Electrical engineering
Publisher DOI:
http://dx.doi.org/10.1109/ASPAA.2007.4393045
Item views:
66
Metadata:
text | xml

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