ICA-FX features for classification of singing voice and instrumental sound

Author

Leung, Tat-Wan ; Ngo, Chong-Wah ; Lau, Rynson W.H.

Author_Institution

Dept. of Comput. Sci., City Univ. of Hong Kong, China

Volume

2

fYear

2004

fDate

23-26 Aug. 2004

Firstpage

367

Abstract

This paper describes a new approach in locating the segments of singing voice in pop musical songs. Initially, GLR distance measure is employed to temporally detect the boundaries of singing voices and instrumental sounds. ICA-FX is then adopted to extract the independent components of acoustic features for SVM classification. Experimental results indicate that ICA-FX can improve the classification performance by significantly reducing the independent components that are not related to class label information.

Keywords

audio signal processing; content-based retrieval; information retrieval; music; musical instruments; support vector machines; content based musical information retrieval; independent component analysis; instrumental sounds; pop musical songs; singing voice; support vector machine classification; Acoustic measurements; Acoustic signal detection; Content based retrieval; Data mining; Feature extraction; Independent component analysis; Instruments; Music information retrieval; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on

ISSN

1051-4651

Print_ISBN

0-7695-2128-2

Type

conf

DOI

10.1109/ICPR.2004.1334222

Filename

1334222