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

研究成果: 雜誌貢獻會議論文同行評審

20 引文 斯高帕斯(Scopus)

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.

原文???core.languages.en_GB???
文章編號1699805
頁(從 - 到)157-160
頁數4
期刊Proceedings - International Conference on Pattern Recognition
4
DOIs
出版狀態已出版 - 2006
事件18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
持續時間: 20 8月 200624 8月 2006

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