Abstract / truncated to 115 words (read the full abstract)

When listening to music, some humans can easily recognize which instruments play at what time or when a new musical segment starts, but cannot describe exactly how they do this. To automatically describe particular aspects of a music piece – be it for an academic interest in emulating human perception, or for practical applications –, we can thus not directly replicate the steps taken by a human. We can, however, exploit that humans can easily annotate examples, and optimize a generic function to reproduce these annotations. In this thesis, I explore solving different music perception tasks with deep learning, a recent branch of machine learning that optimizes functions of many stacked nonlinear operations – referred ... toggle 18 keywords

machine learning – deep learning – multilayer perceptron – convolutional neural network – music information retrieval – music detection – speech detection – vocal activity detection – sequence labelling – music similarity estimation – onset detection – event detection – boundary detection – structural segmentation – music segmentation – singing voice detection – data augmentation – weak labels

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