Musical genre classification using higher-order statistics

In this study, we examine the effects of higher order statistics of timbral features to improve performance of genre classification. It was seen that the first and second order statistics of the features extracted, in this research, is not as discriminative as the third and forth order statistics of the features. For the purpose of designing a classifier, which could be used for real time applications in future studies, randomly taken 3 second-long segments are used for classification. Out of 225 songs from 3 genres, 180 of them are used for training and 45 of them are used for testing. Five different lists that are created using different train and test sets are used to reduce the dependency of the results to the test set while increasing the number of validation data. Average values of validation test results are compared with the results of the similar works, which are based on MIDI format, using the same data set.