Title :
Automatic Vocal Segments Detection in Popular Music
Author :
Liming Song ; Ming Li ; Yonghong Yan
Author_Institution :
Key Lab. of Speech Acoust. & Content Understanding, Inst. of Acoust., Beijing, China
Abstract :
We propose a technique for the automatic vocal segments detection in an acoustical polyphonic music signal. We use a combination of several characteristics specific to singing voice as the feature and employ a Gaussian Mixture Model (GMM) classifier for vocal and non-vocal classification. We have employed a pre-processing of spectral whitening and archived a performance of 81.3% over the RWC popular music dataset.
Keywords :
Gaussian processes; acoustic signal detection; mixture models; music; signal classification; speech recognition; GMM classifier; Gaussian mixture model classifier; RWC popular music dataset; acoustical polyphonic music signal; automatic vocal segment detection; nonvocal classification; singing voice; spectral whitening preprocessing; vocal classification; Feature extraction; Harmonic analysis; Mel frequency cepstral coefficient; Music; Speech; GMM; LFPC; MFCC; Singing Voice Detection; Spectral Whitening; Subband Energy Variance; Vocal Segments Detection;
Conference_Titel :
Computational Intelligence and Security (CIS), 2013 9th International Conference on
Conference_Location :
Leshan
Print_ISBN :
978-1-4799-2548-3
DOI :
10.1109/CIS.2013.80
