Genre musical

Résumé

thumb|Tango. D'après l'encyclopédie Larousse, le genre musical est un . Le genre musical est un concept sans limites précises, il est compliqué d'établir une liste exhaustive des genres. La dénomination d’un genre peut venir d’une expression qui a marqué une scène musicale (Krautrock), de techniques ou sources sonores utilisées par le genre musical populaire (techno, synthpop), de son origine géographique (Miami bass, UK garage), ou de l’intention que porte le style (rock psychédélique). Un genre musical peut être vocal ou instrumental. Critères pour définir un genre musical Selon le genre envisagé, diverses caractéristiques matérielles ou humaines peuvent être prises en considération. Source sonore La source sonore est souvent déterminante pour appréhender un genre : celle-ci définit les instruments, les voix, les formations ou les effectifs d

Source officielle

https://fr.wikipedia.org/wiki/Genre_musical

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Retracer l'histoire des genres et des styles musicaux pratiqués au 20e siècle dans le domaine de la musique savante. Comprendre et analyser les différents langages et courants d'idées. Connaître les œuvres majeures du répertoire classique du 20e siècle ainsi que les principaux textes théoriques.

Ce cours propose d'étudier la présence de particularités musicales extra-européennes et populaires dans la musique classique (17e-20e siècles). Il débouche sur la rédaction d'un travail de recherche en groupe (ou individuel) sur un sujet lié à cette thématique.

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Cours associés

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Séances de cours associées

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On the design of audio features robust to the album-effect for music information retrieval

Nicolas Scaringella

Short-term spectral features – and most notably Mel-Frequency Cepstral Coefficients (MFCCs) – are the most widely used descriptors of audio signals and are deployed in a majority of state-of-the-art Music Information Retrieval (MIR) systems. These descriptors have however demonstrated their limitations in the context of speech processing when training and testing conditions of the system do not match, like e.g. in noisy conditions or under a channel mismatch. A related problem has been observed in the context of music processing. It has indeed been hypothesized that MIR algorithms relying on the use of short-term spectral features were unexpectedly picking up on similarities in the production/mastering qualities of music albums. This problem has been referred to as the album-effect in the literature though it has never been studied in depth. It is showed in this thesis how the album-effect relates to the problem of channel mismatch. A measure of robustness to the album-effect is proposed and channel normalization techniques borrowed from the speech processing community are evaluated to help at improving the robustness of short-term spectral features. Alternatively, longer-term features describing critical-band specialized temporal patterns (TRAPs) are adapted to the context of music processing. It is shown how such features can help at describing either timbre or rhythm content depending on the scale considered for analysis and how robust they are to the album-effect. Contrarily to more classic short-term spectral descriptors, TRAP-based features encode some form of prior knowledge of the problem considered through a trained feature extraction chain. The lack of appropriately annotated datasets raises however some new issues when it comes to training the feature extraction chain. Advanced unsupervised learning strategies are considered in this thesis and evaluated against more traditional supervised approaches relying on coarse-grained annotations such as music genres. Specialized learning strategies and specialized architectures are also proposed to compensate for some inherent variability of the data due either to album-related factors or to the dependence of music signals to the tempo of the performance.

EPFL2009

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Automatic genre classification of music content

Nicolas Scaringella, Giorgio Zoia

This paper reviews the state-of-the-art in automatic genre classification of music collections through three main paradigms: expert systems, unsupervised classification, and supervised classification. The paper discusses the importance of music genres with their definitions and hierarchies. It also presents techniques to extract meaningful information from audio data to characterize musical excerpts. The paper also presents the results of new emerging research fields and techniques that investigate the proximity of music genres

2006

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The pleasantness of sensory dissonance is mediated by musical style and expertise (vol 9, 1070, 2019)

Fabian Claude Moss, Markus Franz Josef Neuwirth, Tudor Popescu, Martin Alois Rohrmeier

2019

Chargement

On the design of audio features robust to the album-effect for music information retrieval

Nicolas Scaringella

EPFL2009