Decision time horizon for music genre classification using short-time features

In this paper music genre classification has been explored with spe-cial emphasis on the decision time horizon and ranking of tapped-delay-line short-time features. Late information fusion as e.g. ma-jority voting is compared with techniques of early information fu-sion1 such as dynamic PCA (DPCA). The most frequently sug-gested features in the literature were employed including mel-frequency cepstral coefficients (MFCC), linear prediction coeffi-cients (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. 1