Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis

Authors:
Jia-Ching Wang;Jhing-Fa Wang;Cai-Bei Lin;Kun-Ting Jian;Wai-He Kuok
Affiliations:
National Cheng Kung University;National Cheng Kung University;National Cheng Kung University;National Cheng Kung University;National Cheng Kung University
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

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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.