MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Journal of Intelligent Information Systems
Discriminant feature analysis for music timbre recognition and automatic indexing
MCD'07 Proceedings of the 3rd ECML/PKDD international conference on Mining complex data
Blind music timbre source isolation by multi- resolution comparison of spectrum signatures
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Recognition of instrument timbres in real polytimbral audio recordings
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
All that jazz in the random forest
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Playing in unison in the random forest
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
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The increasing needs of content-based automatic indexing for large musical repositories have led to extensive investigation in musical sound pattern recognition. Numerous acoustical sound features have been developed to describe the characteristics of a sound piece. Many of these features have been successfully applied to monophonic sound timbre recognition. However, most of those features failed to describe enough characteristics of polyphonic sounds for the purpose of classification, where sound patterns from different sources are overlapping with each other. Thus, sound separation technique is needed to process polyphonic sounds into monophonic sounds before feature extraction. In this paper, we proposed a novel sound source separation and estimation system to isolate sound sources by maximum likelihood fundamental frequency estimation and pattern matching of a harmonic sequence in our feature database.