Abstract: Recently many research has been conducted to
retrieve pertinent parameters and adequate models for automatic
music genre classification. In this paper, two measures based upon
information theory concepts are investigated for mapping the features
space to decision space. A Gaussian Mixture Model (GMM) is used
as a baseline and reference system. Various strategies are proposed
for training and testing sessions with matched or mismatched
conditions, long training and long testing, long training and short
testing. For all experiments, the file sections used for testing are
never been used during training. With matched conditions all
examined measures yield the best and similar scores (almost 100%).
With mismatched conditions, the proposed measures yield better
scores than the GMM baseline system, especially for the short testing
case. It is also observed that the average discrimination information
measure is most appropriate for music category classifications and on
the other hand the divergence measure is more suitable for music
subcategory classifications.