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The Cognitive Neuroscience of Music$
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Isabelle Peretz and Robert J. Zatorre

Print publication date: 2003

Print ISBN-13: 9780198525202

Published to Oxford Scholarship Online: March 2012

DOI: 10.1093/acprof:oso/9780198525202.001.0001

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Learning and Perceiving Musical Structures: Further Insights from Artificial Neural Networks

Learning and Perceiving Musical Structures: Further Insights from Artificial Neural Networks

Chapter:
(p.109) Chapter 8 Learning and Perceiving Musical Structures: Further Insights from Artificial Neural Networks
Source:
The Cognitive Neuroscience of Music
Author(s):

Barbara Tillmann

Jamshed J. Bharucha

Emmanuel Bigand

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198525202.003.0008

This chapter summarizes the applications of artificial neural networks to music cognition, notably to the learning and perceiving of musical structures. It presents a hierarchical self-organizing model that learns basic regularities of the Western tonal system by mere exposure and simulates tonal acculturation. After learning, the model simulates a variety of experiments dealing with the processing of tone, chord, and key relationships. It then provides a parsimonious account of these data sets by postulating activation as the unifying mechanism underlying various cognitive tasks. The modelling of music cognition presented in this chapter is restricted to behavioural data. Nevertheless, the computational processes are based on fundamental neural constraints. Future developments of artificial networks simulating neuropsychological cases and establishing direct links to neural correlates will contribute to enhance the understanding of mechanisms of music perception.

Keywords:   artificial neural networks, learning, perceiving, musical structures, music perception

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