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Méthodes d'apprentissage automatique pour la transcription automatique de la batterie

Abstract : This thesis focuses on learning methods for automatic transcription of the battery. They are based on a transcription algorithm using a non-negative decomposition method, NMD. This thesis raises two main issues: the adaptation of methods to the analyzed signal and the use of deep learning. Taking into account the information of the signal analyzed in the model can be achieved by their introduction during the decomposition steps. A first approach is to reformulate the decomposition step in a probabilistic context to facilitate the introduction of a posteriori information with methods such as SI-PLCA and statistical NMD. A second approach is to implement an adaptation strategy directly in the NMD: the application of modelable filters to the patterns to model the recording conditions or the adaptation of the learned patterns directly to the signal by applying strong constraints to preserve their physical meaning. The second approach concerns the selection of the signal segments to be analyzed. It is best to analyze segments where at least one percussive event occurs. An onset detector based on a convolutional neural network (CNN) is adapted to detect only percussive onsets. The results obtained being very interesting, the detector is trained to detect only one instrument allowing the transcription of the three main drum instruments with three CNNs. Finally, the use of a CNN multi-output is studied to transcribe the part of battery with a single network.
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Submitted on : Monday, June 15, 2020 - 9:58:13 AM
Last modification on : Tuesday, October 27, 2020 - 2:30:23 PM


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  • HAL Id : tel-02867834, version 1


Céline Jacques. Méthodes d'apprentissage automatique pour la transcription automatique de la batterie. Acoustique [physics.class-ph]. Sorbonne Université, 2019. Français. ⟨NNT : 2019SORUS150⟩. ⟨tel-02867834⟩



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