Musical Networks: Parallel Distributed Perception and Performance
Niall Griffith, Peter M.. Todd, 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 algorithm allow analysis applied approach architecture artificial Association beat cells chord classifying Cognitive complex components composition Computer Music CONCERT Conference connectionist connections consists context continuous corresponding create critics described developed direct distributed duration effect error example expectations experiments Figure frequency function given harmonic human images important input interval inverse model Journal layer learning Level listeners measure mechanism melody memory method metrical motion neural network occur original oscillator output particular patterns perception performance period phrase pitch possible predictions preferences presented Press problem Proceedings produce range relative represent representation response rhythm rhythmic rules scale Science selection sequence shown shows signal similar simple simulations single song sound space stream structure Table temporal theory Todd tonal tone traces training set units University weights