Psychologie de la musique

Résumé

vignette|Modèle psychologique de la perception des hauteurs. La psychologie de la musique est une branche de la psychologie expérimentale consacrée à l'étude de la perception des sons musicaux. C'est un domaine des neurosciences annexe de la musicologie et de la musicothérapie. Approches distingue cinq approches dans les études de psychologie de la musique :

La psychologie de la forme envisage la musique comme succession de motifs ;

la psychoacoustique étudie les contraintes et les possibilités ouvertes par le système auditif ;

l'approche grammaticale traite la musique comme une forme de langage, et en recherche les règles de formation ;

la psychologie de la performance étudie la façon de pratiquer une activité musicale ;

l'approche esthétique étudie les associations entre musique et sentiments.

Elle conclut que les approches psychoacoustiques et de psychologie cognitive ont grandement progressé, mais qu'elles ne peuvent guère faire évoluer l'approche esthétique

Source officielle

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

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (2)

Publications associées

Chargement

Personnes associées

Chargement

Unités associées

Chargement

Concepts associés (6)

Concepts associés

Chargement

Cours associés (2)

Cours associés

Chargement

Séances de cours associées

Chargement

Music Cognition: The Complexity of Musical Structure

Christoph Finkensiep, Mathieu Giraud, Daniel Harasim, Martin Alois Rohrmeier

Music is highly complex and provides a rich variety of insights into the human mind, its mental structures, and processes. Experienced musicians are able to create complex structures in real time effortlessly, yet there is at present no successful model of full musical structure. The integration of different musical aspects such as melody, rhythm, voice leading, and form as well as the representation of long-term structure are particularly challenging. To open new possibilities for the study of higher-order structure in music and its perceptual correlates, cognitive music research would benefit from further mutual integration of theoretical, mathematical, computational, and psychological research, similar to advancements in linguistics. This symposium therefore focuses on the formal understanding and empirical investigation of music-theoretically motivated research questions in music cognition. It connects perspectives from music theory, behavioral research, corpus research, and computational modeling, and aims to initiate interdisciplinary discussions about the currently most challenging topics related to the cognition of higher-order structures in music.

2021

Chargement

, ,

Pitch-class distributions are of central relevance in music information retrieval, computational musicology and various other fields, such as music perception and cognition. However, despite their structure being closely related to the cognitively and musically relevant properties of a piece, many existing approaches treat pitch-class distributions as fixed templates. In this paper, we introduce the Tonal Diffusion Model, which provides a more structured and interpretable statistical model of pitch-class distributions by incorporating geometric and algebraic structures known from music theory as well as insights from music cognition. Our model explains the pitch-class distributions of musical pieces by assuming tones to be generated through a latent cognitive process on the Tonnetz, a well-established representation for harmonic relations. Specifically, we assume that all tones in a piece are generated by taking a sequence of interval steps on the Tonnetz starting from a unique tonal origin. We provide a description in terms of a Bayesian generative model and show how the latent variables and parameters can be efficiently inferred. The model is quantitatively evaluated on a corpus of 248 pieces from the Baroque, Classical, and Romantic era and describes the empirical pitch-class distributions more accurately than conventional template-based models. On three concrete musical examples, we demonstrate that our model captures relevant harmonic characteristics of the pieces in a compact and interpretable way, also reflecting stylistic aspects of the respective epoch.

2020

Chargement