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Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
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A predictive coding approach to modelling the perceived complexity of popular music drum patterns
1Music Department, Lucerne University of Applied Sciences and Arts, Arsenalstrasse 28a, 6010, Kriens, Switzerland.
Heliyon
|May 1, 2023
Summary
This study introduces a novel method to measure music complexity using surprisal from predictive coding theory. The approach accurately estimates perceived complexity in popular music drum patterns.
Area of Science:
- Music cognition
- Computational musicology
- Psychoacoustics
Background:
- Predictive coding theory explains perception as minimizing prediction errors (surprisal).
- Music complexity is a key aspect of aesthetic experience, but objective measurement remains challenging.
- Previous models of musical complexity often lack a strong theoretical basis in perception.
Purpose of the Study:
- To develop and validate a computational method for estimating the perceived complexity of popular music drum patterns.
- To operationalize the concept of surprisal from predictive coding theory for musical stimuli.
- To provide a tool for analyzing and understanding the structural properties of drum patterns that contribute to perceived complexity.
Main Methods:
- Approximating a listener's internal model of drum patterns using Bayesian learning and enculturation principles.
- Quantifying surprisal by measuring discrepancies between the internal model's predictions and the actual drum pattern.
- Estimating pattern complexity based on the calculated surprisal values.
Main Results:
- The proposed method demonstrated a good fit with empirical measurements of perceived complexity for 40 popular music drum patterns.
- Simulations suggest the method can predict perceived complexity with high accuracy (good fit) across the Western popular music repertoire.
- The R script implementation allows for future estimation of complexity in diverse drum patterns.
Conclusions:
- Surprisal, derived from predictive coding, effectively captures essential aspects of perceived musical complexity.
- The developed method offers a theoretically grounded and empirically validated approach to quantifying music complexity.
- This work lays the foundation for a general theory of perceived complexity in auditory stimuli.
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