PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Pattern Recognition Approach for Music Style Identification Using Shallow Statistical Descriptors
Pedro J. Ponce de León and José Iñesta
IEEE Transactions on Systems Man and Cybernetics 2004.

Abstract

In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval (MIR) applications. One challenging task in this area is the automatic recognition of musical style, having a number of applications like indexing and selecting musical databases. From melodies symbolically represented as digital scores (standard MIDI files) a number of melodic, harmonic, and rhythmic statistical descriptors are computed and their classification capability assessed in order to build effective description models. A framework for experimenting in this problem is presented, covering the feature extraction, feature selection, and classification stages, in such a way that new features and new musical styles can be easily incorporated and tested. Different classification methods, like Bayesian classifier, nearest neighbours, and self-organising maps are applied. The performance of such algorithms against different description models and parameters is analysed for two particular musical styles, like jazz and classical, used as an initial benchmark for our system.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:753
Deposited By:José Iñesta
Deposited On:30 December 2004