Feature selection for automatic classification of musical instrument sounds

Abstract

In this paper, we carry out a study on classification of musical instr uments using a small set of features selected from a broad range of extracted ones by sequential forward feature selection method. Firstly, we extract 58 features for each record in the music database of 351 sound files. Then, the sequential forward selection method is adopted to choose the best feature set to achieve high classification accuracy. Three different classification techniques have been tested out and an accuracy of up to 93% can be achieved by using 19 features.