Multiclass MTS for saxophone timbre quality inspection using waveform-shape-based features
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Algorithmic compositions based on discovered musical patterns
Multimedia Tools and Applications
Feature selection in a cartesian ensemble of feature subspace classifiers for music categorisation
Proceedings of 3rd international workshop on Machine learning and music
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In the field of computer music, pattern recognition algorithms are very relevant for music information retrieval 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 musical instrument digital interface 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 neighbors, and self-organizing maps, are applied. The performance of such algorithms against different description models and parameters is analyzed for two particular musical styles, jazz and classical, used as an initial benchmark for our system