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Meter as Mechanism: A Neural Network Model that Learns Metrical Patterns

 

作者: MICHAEL GASSER,   DOUGLAS ECK,   ROBERT PORT,  

 

期刊: Connection Science  (Taylor Available online 1999)
卷期: Volume 11, issue 2  

页码: 187-216

 

ISSN:0954-0091

 

年代: 1999

 

DOI:10.1080/095400999116331

 

出版商: Taylor & Francis Group

 

关键词: Rhythm;Meter;Periodicity;Oscillators;Synchronization;Speech;Music

 

数据来源: Taylor

 

摘要:

One kind of prosodic structure that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of the timing of both music and speech show that the nested periodicities that define metrical structure can be quite noisy in time. What kind of system could produce or perceive such variable metrical timing patterns? And what would it take to be able to store and reproduce particular metrical patterns from long-term memory? We have developed a network of coupled oscillators that both produces and perceives patterns of pulses that conform to particular meters. In addition, beginning with an initial state with no biases, it can learn to prefer the particular meter that it has been previously exposed to.

 

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