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BOOSTING FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL: AN EVALUATION
Tokyo, Japan August 22-August 25
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICME.2001.12378922001 IEEE International Conference on ...
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Guodong Guo, Microsoft Research China 5F, Beijing Sigma Center, No. 49, Zhichun Road
Hong-Jiang Zhang, Microsoft Research China 5F, Beijing Sigma Center, No. 49, Zhichun Road
Stan Z. Li, Microsoft Research China 5F, Beijing Sigma Center, No. 49, Zhichun Road
In this paper, we evaluate a recently proposed algorithm in machine learning called AdaBoost for content-based audio classification and retrieval. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. Our focus is to evaluate its classification and retrieval accuracy as compared with other methods. The Muscle Fish audio database of 409 sounds is used for the evaluation with perceptual and cepstral features.
Citation:
Guodong Guo, Hong-Jiang Zhang, Stan Z. Li, "BOOSTING FOR CONTENT-BASED AUDIO CLASSIFICATION AND RETRIEVAL: AN EVALUATION," icme, pp.253, 2001 IEEE International Conference on Multimedia and Expo (ICME'01), 2001
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