Hybrid Support Vector Machine and General Model Approach for Audio Classification

Authors:
Xin He;Ling Guo;Xianzhong Zhou;Wen Luo
Affiliations:
School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, P.R. China;School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, P.R. China;School of Management Engineering, Nanjing University, Nanjing, Jiangsu, 210093, P.R. China;School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, 210094, P.R. China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Year:
2007

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Abstract

In recent years, the searching and indexing techniques for multimedia data are getting more attention in the area of multimedia databases. As many research works were done on the content-based retrieval of image and video data, less attention was received to the content-based retrieval of audio data. Audio is one of important multimedia information and there is a growing need for automatic audio indexing and retrieval techniques in recent years. Audio data contain abundant semantics and the audio signal processing can reduce computational complexity, so effective and efficient indexing and retrieval techniques for audio data are getting more attention. In this paper, problems of audio retrieval are discussed firstly. Then, main audio characteristics and features are introduced. Finally the combination of Support Vector Machine and General Model is described and the hybrid model is used in audio retrieval. Experiments show that the hybrid model is effective for audio classification.