Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 269361, 10 pages
Research Article

Discrete and Dual Tree Wavelet Features for Real-Time Speech/Music Discrimination

1Graduate School of Natural and Applied Sciences, Dokuz Eylul University, 35160 Buca, İzmir, Turkey
2Department of Electronics and Telecommunications Engineering, Izmir University of Economics, 35330 Balçova, İzmir, Turkey
3Department of Electrical and Electronics Engineering, Yaşar University, 35100 Bornova, İzmir, Turkey

Received 5 January 2011; Accepted 1 March 2011

Academic Editor: P. C. Yuen

Copyright © 2011 Timur Düzenli and Nalan Özkurt. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The performance of wavelet transform-based features for the speech/music discrimination task has been investigated. In order to extract wavelet domain features, discrete and complex orthogonal wavelet transforms have been used. The performance of the proposed feature set has been compared with a feature set constructed from the most common time, frequency and cepstral domain features such as number of zero crossings, spectral centroid, spectral flux, and Mel cepstral coefficients. The artificial neural networks have been used as classification tool. The principal component analysis has been applied to eliminate the correlated features before the classification stage. For discrete wavelet transform, considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. The dual tree wavelet transform has also demonstrated a successful performance both in terms of accuracy and time consumption. Finally, a real-time discrimination system has been implemented using the Daubhecies8 wavelet which has the best accuracy.