Genre classification of compressed audio data

This paper deals with the musical genre classification problem, starting from a set of features extracted directly from MPEG-1 layer III compressed audio data. The automatic classification of compressed audio signals into a short hierarchy of musical genres is explored. More specifically, three feature sets for representing timbre, rhythmic content and energy content are proposed for a four leafs tree genre hierarchy. The adopted set of features are computed from the spectral information available in the MPEG decoding stage. The performance and relative importance of the proposed approach is investigated by training a classification model using the audio collections proposed in musical genre contests. We also used an optimization strategy based on genetic algorithms. The results are comparable to those obtained by PCM-based musical genre classification systems.