Feature set for Philippine Gong Music classification by indigenous group

摘要

In this study, the feature set which brought about the highest classification accuracy for sorting Philippine Gong Music clips by indigenous group was sought. The features reflected Timbre, Loudness, Rhythm and Melody-and-Pitch. Two classifiers were used: Support Vector Machines and Neural Networks. Sequential Feature Selection was used to optimize the feature set. The highest accuracy achieved was 90.83% when the combination of SVM, 30s clips and the full Timbre feature set (64 features) was used. K-means clustering was also done to find similarities among the gong styles of the different groups.