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http://dx.doi.org/10.3745/KIPSTB.2012.19B.1.019

Audio Segmentation and Classification Using Support Vector Machine and Fuzzy C-Means Clustering Techniques  

Nguyen, Ngoc (울산대학교 컴퓨터공학과)
Kang, Myeong-Su (울산대학교 컴퓨터정보통신공학과)
Kim, Cheol-Hong (전남대학교 전자컴퓨터공학부)
Kim, Jong-Myon (울산대학교 컴퓨터정보통신공학부)
Publication Information
The KIPS Transactions:PartB / v.19B, no.1, 2012 , pp. 19-26 More about this Journal
Abstract
The rapid increase of information imposes new demands of content management. The purpose of automatic audio segmentation and classification is to meet the rising need for efficient content management. With this reason, this paper proposes a high-accuracy algorithm that segments audio signals and classifies them into different classes such as speech, music, silence, and environment sounds. The proposed algorithm utilizes support vector machine (SVM) to detect audio-cuts, which are boundaries between different kinds of sounds using the parameter sequence. We then extract feature vectors that are composed of statistical data and they are used as an input of fuzzy c-means (FCM) classifier to partition audio-segments into different classes. To evaluate segmentation and classification performance of the proposed SVM-FCM based algorithm, we consider precision and recall rates for segmentation and classification accuracy for classification. Furthermore, we compare the proposed algorithm with other methods including binary and FCM classifiers in terms of segmentation performance. Experimental results show that the proposed algorithm outperforms other methods in both precision and recall rates.
Keywords
Audio Segmentation; Audio Classification; Support Vector Machine; Feature Extraction; Fuzzy C-means Clustering;
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