Musical Networks: Parallel Distributed Perception and Performance
Niall Griffith, Peter M. Todd, Research Scientist Peter M Todd
MIT Press, 1999 - Computers - 385 pages
This volume presents the most up-to-date collection of neural network models of music and creativity gathered together in one place. Chapters by leaders in the field cover new connectionist models of pitch perception, tonality, musical streaming, sequential and hierarchical melodic structure, composition, harmonization, rhythmic analysis, sound generation, and creative evolution. The collection combines journal papers on connectionist modeling, cognitive science, and music perception with new papers solicited for this volume. It also contains an extensive bibliography of related work.
Contributors: Shumeet Baluja, M. I. Bellgard, Michael A. Casey, Garrison W. Cottrell, Peter Desain, Robert O. Gjerdingen, Mike Greenhough, Niall Griffith, Stephen Grossberg, Henkjan Honing, Todd Jochem, Bruce F. Katz, John F. Kolen, Edward W. Large, Michael C. Mozer, Michael P. A. Page, Caroline Palmer, Jordan B. Pollack, Dean Pomerleau, Stephen W. Smoliar, Ian Taylor, Peter M. Todd, C. P. Tsang, Gregory M. Werner.
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Development of Tonal Centres and Abstract Pitch as Categorizations
Understanding Musical Sound with Forward Models and Physical
Resonance and the Perception of Musical Meter
A Critical View
Apparent Motion in Music?
Modelling the Perception of Musical Sequences with Selforganizing
An Ear for Melody
Harmonizing Music the Boltzmann Way
Reduced Memory Representations for Music
Frankensteinian Methods for Evolutionary Music Composition
A Dream Come True
activation algorithmic composition architecture artificial Artificial Intelligence artificial neural network ARTMAP auditory back-propagation beat Cambridge chorale chord classifying complex components Computer Music Association Computer Music Conference CONCERT Connectionism connectionist models context D.G. Loy Eds delta rule Desain described direct inverse model duration encoding entrainment evolution example expectations F2 cell Figure frequency function genetic algorithms Grossberg group layer harmonic images improvisations International Computer Music interval Krumhansl learning Lerdahl Level listeners Longuet-Higgins mechanism melody memory metrical structure motion trace Mozer Music and Connectionism Music Cognition Music Perception musical sequences neural network node octave oscillator output P.M. Todd parameter performance error phrase pitch pitch class predictions presented problem produce psychological RAAM represent representation rhythm rhythmic Rumelhart scale self-organizing signal simulations song spectral stimulus supervised learning temporal test set theory time-span Todd & D.G. tonal tone top-down training set transition table units vector