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

被引：0

作者：

Wang, Jia-Ching ^{[1
]}

Wang, Jhing-Fa ^{[1
]}

Lin, Cai-Bei ^{[1
]}

Jian, Kun-Ting ^{[1
]}

Kuok, Wai-He ^{[1
]}

机构：

[1] Natl Cheng Kung Univ, Dept Elect Engn, 1 Univ Rd, Tainan 70101, Taiwan

来源：

18TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 4, PROCEEDINGS | 2006年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：157 / +

页数：2

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