Composers Classification by Computing Information Quantity and Segmenting Music Score

Abstract

Classifying music into genres or composers is a classical task in music classification. In this paper, we propose a novel method for classifying music composers. In general, we use whole data in the music piece when we estimate the composer. However, this research considers estimating the composer after using the segmented data rather than whole the music data. In our proposed method, we divide the data into short segments and use majority voting. For this experiment, we collected 15 piano pieces from 5 composers and used a total of 75 pieces of MIDI data. The experiment shows that our proposed method produces a higher number of correct answers than the results of previous studies using the information of all the pieces. We also show that our proposed method can also be used for a detailed analysis of musical pieces.