L'analyse probabiliste en composantes latentes et ses adaptations aux signaux musicaux : application à la transcription automatique de musique et à la séparation de sources

Abstract : Automatic music transcription consists in automatically estimating the notes in a recording, through three attributes: onset time, duration and pitch. To address this problem, there is a class of methods which is based on the modeling of a signal as a sum of basic elements, carrying symbolic information. Among these analysis techniques, one can find the probabilistic latent component analysis (PLCA). The purpose of this thesis is to propose variants and improvements of the PLCA, so that it can better adapt to musical signals and th us better address the problem of transcription. To this aim, a first approach is to put forward new models of signals, instead of the inherent model 0 PLCA, expressive enough so they can adapt to musical notes having variations of both pitch and spectral envelope over time. A second aspect of this work is to provide tools to help the parameters estimation algorithm to converge towards meaningful solutions through the incorporation of prior knowledge about the signals to be analyzed, as weil as a new dynamic model. Ali the devised algorithms are applie to the task of automatic transcription. They can also be directly used for source separation, which consists in separating several sources from a mixture, and Iwo applications are put forward in this direction
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Benoît Fuentes. L'analyse probabiliste en composantes latentes et ses adaptations aux signaux musicaux : application à la transcription automatique de musique et à la séparation de sources. Traitement du signal et de l'image [eess.SP]. Télécom ParisTech, 2013. Français. ⟨NNT : 2013ENST0011⟩. ⟨tel-01337630⟩

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