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Machine Learning and Applications, Fourth International Conference on (2009)
Miami Beach, Florida
Dec. 13, 2009 to Dec. 15, 2009
ISBN: 978-0-7695-3926-3
pp: 75-78
In this paper, we propose a new general low-level feature representation for audio signals. Our approach, called Dominant Audio Descriptor is inspired by the MPEG-7 Dominant Color Descriptor. It is based on clustering timelocal features and identifying dominant components. The features used to illustrate this approach are the well-known Mel Frequency Cepstral Coefficients. The performance of the proposed framework is evaluated on audio classification and retrieval tasks. In particular, the experiments are performed on a benchmark music data set. The results are compared to those previously obtained on the same data base. We show that our approach improved classification and retrieval results by more then 3%, and for the case of retrieval reached almost perfect retrieval rate of 99:36%. In addition, the paper presents comparative results against several state of the art classifiers, such as Hidden Markov Models, Support Vector Machines and k-Nearest Neighbors.

H. Frigui, O. Missaoui and A. Fadeev, "Dominant Audio Descriptors for Audio Classification and Retrieval," Machine Learning and Applications, Fourth International Conference on(ICMLA), Miami Beach, Florida, 2009, pp. 75-78.
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