Speech/music discrimination based on a new warped LPC-based feature and linear discriminant analysis
Automatic discrimination of speech and music is an important tool in many multimedia applications. The paper presents a low complexity but effective approach, which exploits only one simple feature, called Warped LPC-based Spectral Centroid (WLPCSC). Comparison between WLPC-SC and the classical features proposed in [9] is performed, aiming to assess the good discriminatory power of the proposed feature. The length of the vector for describing the proposed psychoacoustic based feature is reduced to a few statistical values (mean, variance and skewness), which are then transformed to a new feature space by applying LDA with the aim of increasing the classification accuracy percentage. The classification task is performed by applying SVM to the features in the transformed space. The classification results for different types of music and speech show the good discriminating power of the proposed approach.