TR announcement -- neural net music composition

[Mike Mozer]

FTP-host: archive.cis.ohio-state.edu
FTP-file: pub/neuroprose/mozer.musiccomp.ps.Z


Neural network music composition by prediction:  Exploring the benefits of
psychoacoustic constraints and multiscale processing

Michael C. Mozer

Department of Computer Science
and Institute of Cognitive Science
University of Colorado
Boulder, CO  80309-0430

ABSTRACT:  In algorithmic music composition, a simple technique involves
selecting notes sequentially according to a transition table that specifies
the probability of the next note as a function of the previous context.  I
describe an extension of this transition table approach using a recurrent
autopredictive connectionist network called CONCERT.  CONCERT is trained on a
set of pieces with the aim of extracting stylistic regularities.  CONCERT can
then be used to compose new pieces.  A central ingredient of CONCERT is the
incorporation of psychologically-grounded representations of pitch, duration,
and harmonic structure.  CONCERT was tested on sets of examples artificially
generated according to simple rules and was shown to learn the underlying
structure, even where other approaches failed.  In larger experiments, CONCERT
was trained on sets of J. S. Bach pieces and traditional European folk
melodies and was then allowed to compose novel melodies.  Although the
compositions are occasionally pleasant, and are preferred over compositions
generated by a third-order transition table, the compositions suffer from a
lack of global coherence.  To overcome this limitation, several methods are
explored to permit CONCERT to induce structure at both fine and coarse
scales.  In experiments with a training set of waltzes, these methods yielded
limited success, but the overall results cast doubt on the promise of
note-by-note prediction for composition.

32 pages total

TO APPEAR IN _Connection Science_ special issue on music and creativity, 1994.