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Analyse de structures répétitives dans les séquences musicales

Abstract : The work presented in this thesis deals with repetitive structure inference from audio signal using string matching techniques. It aims at proposing and evaluating inference algorithms from a formal study of notions of similarity and repetition in music.We first present a method for representing audio signals by symbolic strings. We introduce alignment tools enabling similarity estimation between such musical strings, and evaluate the application of these tools for automatic cover song identification. We further adapt a bioinformatics indexing technique to allow efficient assessments of music similarity in large-scale datasets. We then introduce several specific repetitive structures and use alignment tools to analyse these repetitions. A first structure, namely the repetition of a chosen segment, is retrieved and evaluated in the context of automatic assignment of missingaudio data. A second structure, namely the major repetition, is defined, retrieved and evaluated regarding expert annotations, and as an alternative indexing method for cover song identification.We finally present the problem of repetitive structure inference as addressed in literature, and propose our own problem statement. We further describe our model and propose an algorithm enabling the identification of a hierarchical music structure. We emphasize the relevance of our method through several examples and by comparing it to the state of the art.
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Submitted on : Saturday, November 22, 2014 - 2:20:51 PM
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  • HAL Id : tel-01086136, version 1


Benjamin Martin. Analyse de structures répétitives dans les séquences musicales. Informatique [cs]. Université Sciences et Technologies - Bordeaux I, 2012. Français. ⟨tel-01086136⟩



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