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Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers

Composers often pick specific instruments to convey a given emotional tone in their music, partly due to their expressive possibilities, but also due to their timbres in specific registers and at given dynamic markings. Of interest to both music psychology and music informatics from a computational...

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Autores principales: McAdams, Stephen, Douglas, Chelsea, Vempala, Naresh N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296353/
https://www.ncbi.nlm.nih.gov/pubmed/28228741
http://dx.doi.org/10.3389/fpsyg.2017.00153
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author McAdams, Stephen
Douglas, Chelsea
Vempala, Naresh N.
author_facet McAdams, Stephen
Douglas, Chelsea
Vempala, Naresh N.
author_sort McAdams, Stephen
collection PubMed
description Composers often pick specific instruments to convey a given emotional tone in their music, partly due to their expressive possibilities, but also due to their timbres in specific registers and at given dynamic markings. Of interest to both music psychology and music informatics from a computational point of view is the relation between the acoustic properties that give rise to the timbre at a given pitch and the perceived emotional quality of the tone. Musician and nonmusician listeners were presented with 137 tones produced at a fixed dynamic marking (forte) playing tones at pitch class D# across each instrument's entire pitch range and with different playing techniques for standard orchestral instruments drawn from the brass, woodwind, string, and pitched percussion families. They rated each tone on six analogical-categorical scales in terms of emotional valence (positive/negative and pleasant/unpleasant), energy arousal (awake/tired), tension arousal (excited/calm), preference (like/dislike), and familiarity. Linear mixed models revealed interactive effects of musical training, instrument family, and pitch register, with non-linear relations between pitch register and several dependent variables. Twenty-three audio descriptors from the Timbre Toolbox were computed for each sound and analyzed in two ways: linear partial least squares regression (PLSR) and nonlinear artificial neural net modeling. These two analyses converged in terms of the importance of various spectral, temporal, and spectrotemporal audio descriptors in explaining the emotion ratings, but some differences also emerged. Different combinations of audio descriptors make major contributions to the three emotion dimensions, suggesting that they are carried by distinct acoustic properties. Valence is more positive with lower spectral slopes, a greater emergence of strong partials, and an amplitude envelope with a sharper attack and earlier decay. Higher tension arousal is carried by brighter sounds, more spectral variation and more gentle attacks. Greater energy arousal is associated with brighter sounds, with higher spectral centroids and slower decrease of the spectral slope, as well as with greater spectral emergence. The divergences between linear and nonlinear approaches are discussed.
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spelling pubmed-52963532017-02-22 Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers McAdams, Stephen Douglas, Chelsea Vempala, Naresh N. Front Psychol Psychology Composers often pick specific instruments to convey a given emotional tone in their music, partly due to their expressive possibilities, but also due to their timbres in specific registers and at given dynamic markings. Of interest to both music psychology and music informatics from a computational point of view is the relation between the acoustic properties that give rise to the timbre at a given pitch and the perceived emotional quality of the tone. Musician and nonmusician listeners were presented with 137 tones produced at a fixed dynamic marking (forte) playing tones at pitch class D# across each instrument's entire pitch range and with different playing techniques for standard orchestral instruments drawn from the brass, woodwind, string, and pitched percussion families. They rated each tone on six analogical-categorical scales in terms of emotional valence (positive/negative and pleasant/unpleasant), energy arousal (awake/tired), tension arousal (excited/calm), preference (like/dislike), and familiarity. Linear mixed models revealed interactive effects of musical training, instrument family, and pitch register, with non-linear relations between pitch register and several dependent variables. Twenty-three audio descriptors from the Timbre Toolbox were computed for each sound and analyzed in two ways: linear partial least squares regression (PLSR) and nonlinear artificial neural net modeling. These two analyses converged in terms of the importance of various spectral, temporal, and spectrotemporal audio descriptors in explaining the emotion ratings, but some differences also emerged. Different combinations of audio descriptors make major contributions to the three emotion dimensions, suggesting that they are carried by distinct acoustic properties. Valence is more positive with lower spectral slopes, a greater emergence of strong partials, and an amplitude envelope with a sharper attack and earlier decay. Higher tension arousal is carried by brighter sounds, more spectral variation and more gentle attacks. Greater energy arousal is associated with brighter sounds, with higher spectral centroids and slower decrease of the spectral slope, as well as with greater spectral emergence. The divergences between linear and nonlinear approaches are discussed. Frontiers Media S.A. 2017-02-08 /pmc/articles/PMC5296353/ /pubmed/28228741 http://dx.doi.org/10.3389/fpsyg.2017.00153 Text en Copyright © 2017 McAdams, Douglas and Vempala. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
McAdams, Stephen
Douglas, Chelsea
Vempala, Naresh N.
Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title_full Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title_fullStr Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title_full_unstemmed Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title_short Perception and Modeling of Affective Qualities of Musical Instrument Sounds across Pitch Registers
title_sort perception and modeling of affective qualities of musical instrument sounds across pitch registers
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5296353/
https://www.ncbi.nlm.nih.gov/pubmed/28228741
http://dx.doi.org/10.3389/fpsyg.2017.00153
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