Index of /~echew/papers/CogSci2001

 Name                    Last modified      Size  Description
 Parent Directory                             -   
 EC-CogSci2001.pdf       08-Jan-2002 17:48   50K  
 reference.txt           05-Aug-2003 22:31  507   

In this directory is the PDF file for a paper titled

"Modeling Tonality: Applications to Music Cognition"
by Elaine Chew (eniale@alum.mit.edu)

The paper was presented at
the 23rd Annual Conference of the Cognitive Science Society, 
Edinburgh, Scotland.  1-4 Aug, 2001.

The paper was published in the conference proceedings.
Click on reference.txt for the BibTeX reference.

This paper is also available at the conference website at
http://www.hcrc.ed.ac.uk/cogsci2001/

THE COMPLETE PAPER, text with figures, can be viewed as a PDF document.
Click on EC-CogSci2001.pdf if you wish to view the paper in PDF format.

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"Modeling Tonality: Applications to Music Cognition"
by Elaine Chew (eniale@alum.mit.edu)

ABSTRACT: Processing musical information is a task many of us perform
effortlessly, and often, unconsciously.  In order to gain a better
understanding of this basic human cognitive ability, we propose a
mathematical model for tonality, the underlying principles for tonal
music.  The model simultaneously incorporates pitch, interval, chord
and key relations.  It generates spatial counterparts for these
musical entities by aggregating musical information.  The model also
serves as a framework on which to design algorithms that can mimic the
human ability to organize musical input.  One such skill is the
ability to determine the key of a musical passage.  This is equivalent
to being able to pick out the most stable pitch in the passage, also
known as ``doh'' in {\em solfege}.  We propose a computational
algorithm that mimics this human ability, and compare its performance
to previous models.  The algorithm is shown to predict the correct key
with high accuracy.  The proposed computational model serves as a
research and pedagogical tool for putting forth and testing hypotheses
about human perception and cognition in music.  By designing efficient
algorithms that mimic human cognitive abilities, we gain a better
understanding of what it is that the human mind can do.