Decision time horizon for music genre classification using short time features

Abstract

In this paper music genre classification has been explored with special emphasis on the decision time horizon and ranking of tapped-delay-line short-time features. Late information fusion as e.g. majority voting is compared with techniques of early information fusion1 such as dynamic PCA (DPCA). The most frequently suggested features in the literature were employed including melfrequency cepstral coefficients (MFCC), linear prediction coefficients (LPC), zero-crossing rate (ZCR), and MPEG-7 features. To rank the importance of the short time features consensus sensitivity analysis is applied. A Gaussian classifier (GC) with full covariance structure and a linear neural network (NN) classifier are used.