Thesis available

[Otavio Augusto Salgado Carpinteiro]

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The following thesis is available via anonymous ftp.

A CONNECTIONIST APPROACH IN MUSIC PERCEPTION

	Otavio A. S. Carpinteiro
	email: otavioc at cogs.susx.ac.uk
	
	Cognitive Science Research Paper CSRP-426
	School of Cognitive & Computing Sciences
	University of Sussex, Brighton, UK

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117 pages. 422107 bytes compressed, 1195561 bytes uncompressed


Paper copies can be ordered from:
	Celia McInnes (celiam at cogs.susx.ac.uk)
	School of Cognitive & Computing Sciences
	University of Sussex
	Falmer, Brighton, UK.

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ABSTRACT:
Little research has been carried  out in order to understand  the
mechanisms  underlying  the   perception  of  polyphonic   music.
Perception of  polyphonic  music involves  thematic  recognition,
that is,  recognition of  instances of  theme through  polyphonic
voices, whether they appear unaccompanied, transposed, altered or
not. There are  many questions  still open  to debate  concerning
thematic recognition in  the polyphonic domain.  One of them,  in
particular,  is  the  question   of  whether  or  not   cognitive
mechanisms of segmentation and thematic reinforcement  facilitate
thematic recognition in polyphonic music.

This dissertation proposes a  connectionist model to  investigate
the role of segmentation  and thematic reinforcement in  thematic
recognition in polyphonic music. The model comprises two  stages.
The first stage consists of a supervised artificial neural  model
to segment  musical  pieces in  accordance  with three  cases  of
rhythmic segmentation. The supervised model is trained and tested
on sets of  contrived patterns, and  successfully applied to  six
musical pieces from J. S. Bach.  The second stage consists of  an
original unsupervised artificial neural model to perform thematic
recognition. The unsupervised model is trained and assessed  on a
four-part fugue from J. S. Bach.

The research carried  out in this  dissertation contributes  into
two distinct  fields. Firstly,  it contributes  to the  field  of
artificial  neural  networks.  The  original  unsupervised  model
encodes and manipulates context information effectively, and that
enables it to perform sequence classification and  discrimination
efficiently. It has application in cognitive domains which demand
classifying either  a set  of  sequences of  vectors in  time  or
sub-sequences within a  unique and large  sequence of vectors  in
time. Secondly, the  research contributes to  the field of  music
perception. The  results  obtained  by  the  connectionist  model
suggest, along with  other important  conclusions, that  thematic
recognition in polyphony is not facilitated by segmentation,  but
otherwise, facilitated by thematic reinforcement.


--

Otavio.

+===========================================================================+
|                                          |                                |
| Otavio Augusto Salgado Carpinteiro       | Phone: +44 (0) 1273 606755     |
| Postgraduate Pigeonholes                 |        ext. 2385               |
| School of Cognitive & Computing Sciences |                                |
| University of Sussex                     | Fax:   +44 (0) 1273 671320     |
| FALMER  -  East Sussex                   |                                |
| BN1  9QH                                 | E-mail:                        |
| England                                  |   otavioc at cogs.sussex.ac.uk    |
|                                          |                                |
+===========================================================================+