Singing voice detection in popular music

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Singing voice detection in popular music
We propose a novel technique for the automatic classification of vocal and non-vocal regions in an acoustic musical signal. Our technique uses a combination of harmonic content attenuation using higher level musical knowledge of key followed by sub-band energy processing to obtain features from the musical audio signal. We employ a Multi-Model Hidden Markov Model (MM-HMM) classifier for vocal and non-vocal classification that utilizes song structure information to create multiple models as opposed to conventional HMM training methods that employ only one model for each class. A statistical hypothesis testing approach followed by an automatic bootstrapping process is employed to further improve the accuracy of classification. An experimental evaluation on a database of 20 popular songs shows the validity of the proposed approach with an average classification accuracy of 86.7% Categories and Subject Descriptors H.5.5 [Information Interfaces and Presentation]: Sound and Music Compu...
Tin Lay Nwe, Arun Shenoy, Ye Wang
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where MM
Authors Tin Lay Nwe, Arun Shenoy, Ye Wang
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