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Models of music signals informed by physics. Application to piano music analysis by non-negative matrix factorization.

Abstract : This thesis introduces new models of music signals informed by the physics of the instruments. While instrumental acoustics and audio signal processing target the modeling of musical tones from different perspectives (modeling of the production mechanism of the sound vs modeling of the generic "morphological'' features of the sound), this thesis aims at mixing both approaches by constraining generic signal models with acoustics-based information. Thus, it is here intended to design instrument-specific models for applications both to acoustics (learning of parameters related to the design and the tuning) and signal processing (transcription). In particular, we focus on piano music analysis for which the tones have the well-known property of inharmonicity. The inclusion of such a property in signal models however makes the optimization harder, and may even damage the performance in tasks such as music transcription when compared to a simpler harmonic model. A major goal of this thesis is thus to have a better understanding about the issues arising from the explicit inclusion of the inharmonicity in signal models, and to investigate whether it is really valuable when targeting tasks such as polyphonic music transcription.
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Submitted on : Tuesday, October 28, 2014 - 10:55:51 AM
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François Rigaud. Models of music signals informed by physics. Application to piano music analysis by non-negative matrix factorization. . Signal and Image Processing. Télécom ParisTech, 2013. English. ⟨NNT : 2013-ENST-0073⟩. ⟨tel-01078150⟩

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