Abstract
With the fast booming of online music repositories, there is a need for content-based automatic indexing which will help users to find their favorite music objects in real time. Recently, numerous successful approaches on musical data feature extraction and selection have been proposed for instrument recognition in monophonic sounds. Unfortunately, none of these methods can be successfully applied to polyphonic sounds. Identification of music instruments in polyphonic sounds is still difficult and challenging, especially when harmonic partials are overlapping with each other. This has stimulated the research on music sound separation and new features development for content-based automatic music information retrieval. Our goal is to build a cooperative query answering system (QAS), for a musical database, retrieving from it all objects satisfying queries like ”find all musical pieces in pentatonic scale with a viola and piano where viola is playing for minimum 20 seconds and piano for minimum 10 seconds”. We use the database of musical sounds, containing almost 4000 sounds taken from the MUMs (McGill University Master Samples), as a vehicle to construct several classifiers for automatic instrument recognition. Classifiers showing the best performance are adopted for automatic indexing of musical pieces by instruments. Our musical database has an FS-tree (Frame Segment Tree) structure representation. The cooperativeness of QAS is driven by several hierarchical structures used for classifying musical instruments.
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Raś, Z.W., Zhang, X., Lewis, R. (2007). MIRAI: Multi-hierarchical, FS-Tree Based Music Information Retrieval System. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_10
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DOI: https://doi.org/10.1007/978-3-540-73451-2_10
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