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Modelling of Musical Perception using Spectral Knowledge Representation.

Steven T Homer1, Nicholas Harley1, Geraint A Wiggins1,2

  • 1Computational Creativity Lab, Artificial Intelligence Research Group, Vrije Universiteit Brussel, Belgium.

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Summary

This study introduces spectral knowledge representation, modeling brain oscillations for perceptual and cognitive insights. It effectively models music perception, including harmonic distance, by representing neural dynamics as waves.

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Hilbert spacecognitive modellingkey affinitykey distanceknowledge representationmusic perceptionneural dynamicsresonancespectral analysis

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Area of Science:

  • Cognitive science
  • Neuroscience
  • Computational modeling

Background:

  • Existing models of cognitive architecture lack a focus on neural oscillatory dynamics.
  • Representing perceptual and cognitive knowledge requires robust methods for dynamic neural processing.

Purpose of the Study:

  • To introduce spectral knowledge representation, a novel approach focused on brain's oscillatory behavior.
  • To demonstrate the model's application in understanding sound and music perception, specifically harmonic distance.

Main Methods:

  • Utilizing literal wave representations to model neural assembly dynamics during input processing.
  • Applying the spectral knowledge representation to sound perception and music theory.

Main Results:

  • The model successfully captures pitch and chord/key distance, aligning with empirical measurements (e.g., Krumhansl and Kessler).
  • Spectral knowledge representation provides a potential underlying mechanism for existing models, such as the toroidal model.

Conclusions:

  • Spectral knowledge representation offers a new framework for understanding cognitive and perceptual processes through neural oscillations.
  • The model's efficacy in music perception suggests broader applications in cognitive science and neuroscience.