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Probing the Underlying Principles of Perceived Immanent Accents Using a Modeling Approach
KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH.
KTH, School of Electrical Engineering and Computer Science (EECS), Speech, Music and Hearing, TMH. Inst Pasteur, France.
Univ Bologna, Dept Educ Studies, Bologna, Italy..
Univ Bologna, Dept Arts, Bologna, Italy..
2019 (English)In: Frontiers in Psychology, ISSN 1664-1078, E-ISSN 1664-1078, Vol. 10, article id 1024Article in journal (Refereed) Published
Abstract [en]

This article deals with the question of how the perception of the "immanent accents" can be predicted and modeled. By immanent accent we mean any musical event in the score that is related to important points in the musical structure (e.g., tactus positions, melodic peaks) and is therefore able to capture the attention of a listener. Our aim was to investigate the underlying principles of these accented notes by combining quantitative modeling, music analysis and experimental methods. A listening experiment was conducted where 30 participants indicated perceived accented notes for 60 melodies, vocal and instrumental, selected from Baroque, Romantic and Posttonal styles. This produced a large and unique collection of perceptual data about the perceived immanent accents, organized by styles consisting of vocal and instrumental melodies within Western art music. The music analysis of the indicated accents provided a preliminary list of musical features that could be identified as possible reasons for the raters' perception of the immanent accents. These features related to the score in different ways, e.g., repeated fragments, single notes, or overall structure. A modeling approach was used to quantify the influence of feature groups related to pitch contour, tempo, timing, simple phrasing, and meter. A set of 43 computational features was defined from the music analysis and previous studies and extracted from the score representation. The mean ratings of the participants were predicted using multiple linear regression and support vector regression. The latter method (using cross-validation) obtained the best result of about 66% explained variance (r = 0.81) across all melodies and for a selected group of raters. The independent contribution of each feature group was relatively high for pitch contour and timing (9.6 and 7.0%). There were also significant contributions from tempo (4.5%), simple phrasing (4.4%), and meter (3.9%). Interestingly, the independent contribution varied greatly across participants, implying different listener strategies, and also some variability across different styles. The large differences among listeners emphasize the importance of considering the individual listener's perception in future research in music perception.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2019. Vol. 10, article id 1024
Keywords [en]
immanent accent, music analysis, melody, modeling, machine learning
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:kth:diva-254504DOI: 10.3389/fpsyg.2019.01024ISI: 000471282500001PubMedID: 31231262OAI: oai:DiVA.org:kth-254504DiVA, id: diva2:1337448
Note

QC 20190715

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-07-15Bibliographically approved

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