Exploring emotions in Bach chorales: a multi-modal perceptual and data-driven study

  • The relationship between music and emotion has been addressed within several disciplines, from more historico-philosophical and anthropological ones, such as musicology and ethnomusicology, to others that are traditionally more empirical and technological, such as psychology and computer science. Yet, understanding the link between music and emotion is limited by the scarce interconnections between these disciplines. Trying to narrow this gap, this data-driven exploratory study aims at assessing the relationship between linguistic, symbolic and acoustic features—extracted from lyrics, music notation and audio recordings—and perception of emotion. Employing a listening experiment, statistical analysis and unsupervised machine learning, we investigate how a data-driven multi-modal approach can be used to explore the emotions conveyed by eight Bach chorales. Through a feature selection strategy based on a set of more than 300 Bach chorales and a transdisciplinary methodology integratingThe relationship between music and emotion has been addressed within several disciplines, from more historico-philosophical and anthropological ones, such as musicology and ethnomusicology, to others that are traditionally more empirical and technological, such as psychology and computer science. Yet, understanding the link between music and emotion is limited by the scarce interconnections between these disciplines. Trying to narrow this gap, this data-driven exploratory study aims at assessing the relationship between linguistic, symbolic and acoustic features—extracted from lyrics, music notation and audio recordings—and perception of emotion. Employing a listening experiment, statistical analysis and unsupervised machine learning, we investigate how a data-driven multi-modal approach can be used to explore the emotions conveyed by eight Bach chorales. Through a feature selection strategy based on a set of more than 300 Bach chorales and a transdisciplinary methodology integrating approaches from psychology, musicology and computer science, we aim to initiate an efficient dialogue between disciplines, able to promote a more integrative and holistic understanding of emotions in music.show moreshow less

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Metadaten
Author:Emilia Parada-Cabaleiro, Anton BatlinerGND, Marcel Zentner, Markus Schedl
URN:urn:nbn:de:bvb:384-opus4-1109903
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/110990
ISSN:2054-5703OPAC
Parent Title (English):Royal Society Open Science
Publisher:The Royal Society
Type:Article
Language:English
Date of first Publication:2023/12/20
Publishing Institution:Universität Augsburg
Release Date:2024/01/26
Volume:10
Issue:12
First Page:230574
DOI:https://doi.org/10.1098/rsos.230574
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)