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Training Set Reduction Based on 2-Gram Feature Statistics for Music Genre Recognition

Too large instance and/or feature number for supervised classification requires higher storage demands and computing time, and also the classification quality may suffer from too huge datasets. In our work we examine the reduction of training instance number in music genre recognition where each instance is mapped to a class described by a corresponding 2-gram estimated from the statistical distribution of musical characteristics. Two approaches are integrated: The removal of outlier instances and the limitation of the maximal training instance number from each 2-gram. The experiments show that it is possible to keep the classification performance and even improve it in many cases despite the strong reduction of instance number.