Content-based audio classification using support vector machines and independent component analysis

Jia Ching Wang, Jhing Fa Wang, Cai Bei Lin, Kun Ting Jian, Wai He Kuok

Research output: Contribution to journalConference articlepeer-review

20 Scopus citations

Abstract

In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate.

Original languageEnglish
Article number1699805
Pages (from-to)157-160
Number of pages4
JournalProceedings - International Conference on Pattern Recognition
Volume4
DOIs
StatePublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: 20 Aug 200624 Aug 2006

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