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dc.contributor.authorSakaue, Daichija
dc.contributor.authorItoyama, Katsutoshija
dc.contributor.authorOgata, Tetsuyaja
dc.contributor.authorOkuno, Hiroshi G.ja
dc.contributor.alternative奥乃, 博ja
dc.description.abstractWe present a Bayesian analysis method that estimates the harmonic structure of musical instruments in music signals on the basis of psychoacoustic evidence. Since the main objective of multipitch analysis is joint estimation of the fundamental frequencies and their harmonic structures, the performance of harmonic structure estimation significantly affects fundamental frequency estimation accuracy. Many methods have been proposed for estimating the harmonic structure accurately, but no method has been proposed that satisfies all these requirements: robust against initialization, optimization-free, and psychoacoustically appropriate and thus easy to develop further. Our method satisfies these requirements by explicitly incorporating Terhardt's virtual pitch theory within a Bayesian framework. It does this by automatically learning the valid weight range of the harmonic components using a MIDI synthesizer. The bounds are termed "overtone corpus." Modeling demonstrated that the proposed overtone corpus method can stably estimate the harmonic structure of 40 musical pieces for a wide variety of initial settings.ja
dc.publisherInformation Processing Society of Japan(情報処理学会)ja
dc.rights© 2013 by the Information Processing Society of Japanja
dc.subjectmultipitch estimationja
dc.subjectharmonic clusteringja
dc.subjectovertone estimationja
dc.subjectmusical instrument soundsja
dc.titleRobust Multipitch Analyzer against Initialization based on Latent Harmonic Allocation using Overtone Corpusja
dc.type.niitypeJournal Articleja
dc.identifier.jtitleJournal of Information Processingja
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