Auditive analysis of the Quartetto per Archi in due tempi (1955) by Bruno Maderna

Addessi, Anna Rita

doi:10.1177/10298649100140s213

Cited by 1 publication

(1 citation statement)

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“…In our previous experimental studies, we analyzed how the listeners (musicians and non-musicians) memorize the overall structure of post-tonal music during listening in real time (Addessi and Caterina, 2005; Addessi, 2010). We observed that the perception of “prominent features” allowed the listeners to perceive several points of segmentations, to divide the piece into the main sections and consequently, to memorize the overall structure of the musical piece.…”

Section: Introductionmentioning

confidence: 99%

Probing the Underlying Principles of Perceived Immanent Accents Using a Modeling Approach

et al. 2019

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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 Post-tonal 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.

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Section: Introductionmentioning

confidence: 99%