Speech/music discrimination using awarped LPC-based feature and a fuzzy expert system for intelligent audio coding

Automatic discrimination of speech and music is an important tool in many multimedia applications. This paper presents an evolutionary fuzzy rules-based speech/music discrimination approach for intelligent audio coding. A low complexity but effective feature, called Warped LPC-based Spectral Centroid (WLPC-SC), is defined for the analysis stage of the discrimination system. The final decision is made by a fuzzy expert system, which improves the accuracy rate provided by a Gaussian Mixture Model (GMM) classifier taking into account the audio labels assigned by the GMM classifier to past audio frames. Comparison between WLPC-SC and most timbral features proposed in [8] is performed, aiming to assess the good discriminatory power of the proposed feature. The accuracy rate improvement due to the fuzzy expert system is also reported. Experimental results reveal that our speech/music discriminator is robust and fast, making it suitable for intelligent audio coding.