Modeling melody recognition using a sequence recognition neural network with meta-level processes

Authors:
Naresh N. Vempala;Anthony S. Maida
Affiliations:
Institute of Cognitive Science, The University of Louisiana at Lafayette, Lafayette, LA;Center for Advanced Computer Studies and Institute of Cognitive Science, The University of Louisiana at Lafayette, Lafayette, LA
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 1
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Abstract

This research models human performance in the Dalla Bella, Peretz, and Aronoff melody recognition study. They compared performance between musicians and nonmusicians in the recognition (and perception) of melodies. They used a gating task to identify three events in the melody perception/recognition process. These were the familiarity emergence point (FEP), the isolation point (IP), and the recognition point (RP). We develop a simulation to model hypothesized cognitive processes underlying these events. The IP is modeled using a winner-take-all connectionist network adapted to operate with temporal input sequences. Meta-level processes examine the dynamic state of the recognition network to model the FEP and the RP.