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Towards global tempo estimation and rhythm-oriented genre classification based on harmonic characteristics of rhythm

Abstract : Automatic detection of the rhythmic structure within music is one of the challenges of the "Music Information Retrieval" research area. The advent of technology dedicated to the arts has allowed the emergence of new musical trends generally described by the term "Electronic/Dance Music" (EDM) which encompasses a plethora of sub-genres. This type of music often dedicated to dance is characterized by its rhythmic structure. We propose a rhythmic analysis of what defines certain musical genres including those of EDM. To do so, we want to perform an automatic global tempo estimation task and a genre classification task based on rhythm. Tempo and genre are two intertwined aspects since genres are often associated with rhythmic patterns that are played in specific tempo ranges. Some so-called "handcrafted" tempo estimation systems have been shown to be effective based on the extraction of rhythm-related characteristics. Recently, with the appearance of annotated databases, so-called "data-driven" systems and deep learning approaches have shown progress in the automatic estimation of these tasks. In this thesis, we propose methods at the crossroads between " handcrafted " and " data-driven " systems. The development of a new representation of rhythm combined with deep learning by convolutional neural network is at the basis of all our work. We present in detail our Deep Rhythm method in this thesis and we also present several extensions based on musical intuitions that allow us to improve our results.
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https://tel.archives-ouvertes.fr/tel-03258671
Contributor : Abes Star :  Contact
Submitted on : Friday, June 11, 2021 - 4:50:08 PM
Last modification on : Tuesday, July 13, 2021 - 2:17:13 PM

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  • HAL Id : tel-03258671, version 1

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Hadrien Foroughmand Aarabi. Towards global tempo estimation and rhythm-oriented genre classification based on harmonic characteristics of rhythm. Musicology and performing arts. Sorbonne Université, 2021. English. ⟨NNT : 2021SORUS018⟩. ⟨tel-03258671⟩

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