A Shallow Description Framework for Musical Style Recognition

Abstract

In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval (MIR). One challenging task within this area is the automatic recognition of musical style, that has a number of applications like indexing and selecting musical databases. In this paper, the classification of monophonic melodies of two different musical styles (jazz and classical) represented symbolically as MIDI files is studied, using different classification methods: Bayesian classifier and nearest neighbour classifier. From the music sequences, a number of melodic, harmonic, and rhythmic statistical descriptors are computed and used for style recognition. We present a performance analysis of such algorithms against different description models and parameters.