Classification of audio signals using gradient-based fuzzy c-means algorithm with divergence measure

Authors:
Dong-Chul Park;Duc-Hoai Nguyen;Seung-Hwa Beack;Sancho Park
Affiliations:
Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;Dept. of Information Engineering, Myong Ji University, Korea;Davan Tech Co., Seongnam, Korea
Venue:
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
Year:
2005

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Abstract

Multimedia databases usually store thousands of audio files such as music, speech and other sounds. One of the challenges in modern multimedia system is to classify and retrieve certain kinds of audio from the database. This paper proposes a novel classification algorithm for a content-based audio retrieval. The algorithm, called Gradient-Based Fuzzy c-Means Algorithm with Divergence Measure (GBFCM(DM)), is a neural network-based algorithm which utilizes the Divergence Measure to exploit the statistical nature of the audio data to improve the classification accuracy. Experiment results confirm that the proposed algorithm outperforms 3.025%-5.05% in accuracy in comparison with conventional algorithms such as the k-Means or the Self-Organizing Map.