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Machine-Learning Based Classification Of Speech And Music

Khan, MKS; Al-Khatib, WG

SPRINGER, MULTIMEDIA SYSTEMS; pp: 55-67; Vol: 12

King Fahd University of Petroleum & Minerals

http://www.kfupm.edu.sa

Summary

The need to classify audio into categories such as speech or music is an important

aspect of many multimedia document retrieval systems. In this paper, we investigate

audio features that have not been previously used in music-speech classification, such

as the mean and variance of the discrete wavelet transform, the variance of Mel-

frequency cepstral coefficients, the root mean square of a lowpass signal, and the

difference of the maximum and minimum zero-crossings. We, then, employ fuzzy C-

means clustering to the problem of selecting a viable set of features that enables better

classification accuracy. Three different classification frameworks have been

studied:Multi-Layer Perceptron (MLP) Neural Networks, radial basis functions (RBF)

Neural Networks, and Hidden Markov Model (HMM), and results of each framework

have been reported and compared. Our extensive experimentation have identified a

subset of features that contributes most to accurate classification, and have shown that

MLP networks are the most suitable classification framework for the problem at hand.

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Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa

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For pre-prints please write to: wasfi@ccse.kfupm.edu.sa

Copyright: King Fahd University of Petroleum & Minerals;http://www.kfupm.edu.sa