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Partial Clustering Using a Time-Varying Frequency Model for Singing Voice Detection.

Abstract : We propose a new method to group partials produced by each instrument of a polyphonic audio mixture. This method works for pitched and harmonic instruments and is specially adapted to singing voice. In our approach, we model time-varying frequencies of partials as a slowly varying frequency plus a sinusoidal modulation. The parameters obtained with this model plus some common Auditory Scene Analysis principles are used to define a similarity measure between partials. This multi-criterion based measure is then used to build the input similarity matrix of a clustering algorithm. Clusters obtained are groups of harmonically related partials. We evaluate the ability of our method to group partials per source when one of the sources is a singing voice. We show that partial clustering is a promising approach for singing voice detection and separation.
Keywords : singing voice CASA
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Contributor : Lise Regnier Connect in order to contact the contributor
Submitted on : Wednesday, January 25, 2012 - 9:50:53 AM
Last modification on : Monday, January 27, 2020 - 6:00:28 PM
Long-term archiving on: : Monday, November 19, 2012 - 2:35:55 PM


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  • HAL Id : hal-00662323, version 1



Lise Regnier, Geoffroy Peeters. Partial Clustering Using a Time-Varying Frequency Model for Singing Voice Detection.. ICASSP, Mar 2010, Dallas, United States. pp.1. ⟨hal-00662323⟩



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