Comparison of Wavelet Based Feature Extraction Methods for Speech/Music Discrimination [conference Object]

Abstract

In this study, 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 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 used in speech/music discrimination such as number of zero crossings, spectral centroid, spectral flux and mel cepstral coefficients. In order to measure the performances of the feature sets for the speech/music discrimination, artificial neural networks have been used as a classification tool. The principal component analysis has been applied to eliminate the correlated features before classification stage. Considering the number of vanishing moments and orthogonality, the best performance is obtained with Daubechies8 wavelet among the other members of the Daubechies family. According to the results the proposed feature set outperforms the traditional ones.