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Probing the Underlying Principles of Perceived Immanent Accents Using a Modeling Approach
Anders Friberg1, Erica Bisesi1,2, Anna Rita Addessi3
1Department of Speech, Music and Hearing, Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden.
Frontiers in Psychology
|June 25, 2019
Summary
This study models how listeners perceive musical accents, finding pitch contour and timing are key predictors. Individual listener strategies significantly influence accent perception across music styles.
Area of Science:
- Music Perception
- Computational Musicology
- Cognitive Science
Background:
- Musical accents are crucial for structure and listener attention.
- Predicting perceived accents requires understanding musical features and listener strategies.
Purpose of the Study:
- To model and predict the perception of "immanent accents" in Western art music.
- To investigate the influence of musical features on accent perception.
- To analyze individual differences in accent perception.
Main Methods:
- A listening experiment with 30 participants rating accents in 60 melodies.
- Music analysis to identify features related to perceived accents.
- Quantitative modeling using machine learning (multiple linear regression, support vector regression) with 43 computational features.
Main Results:
- Support vector regression predicted 66% of the variance in perceived accents (r=0.81).
- Pitch contour (9.6%) and timing (7.0%) were the most influential feature groups.
- Tempo, phrasing, and meter also contributed significantly, but listener strategies varied greatly.
Conclusions:
- Computational models can effectively predict perceived musical accents.
- Pitch contour and timing are primary drivers of accent perception.
- Individual listener differences and musical style impact accent perception, highlighting the need for personalized approaches.
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