PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A shallow description framework for music style recognition
Pedro J. Ponce de León, Carlos Pérez-Sancho and José Manuel Iñesta
Lecture Notes in Computer Science Volume 3138, pp. 876-884, 2004. ISSN 0302-9743

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.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:748
Deposited By:José Iñesta
Deposited On:30 December 2004