Techniques for Generative Melodies Inspired by Music Cognition
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This paper presents a series of algorithmic techniques for melody generation, inspired by models of music cognition. The techniques are designed for interactive composition - and so privilege brevity, simplicity and flexibility over fidelity to the underlying models. The cognitive models canvassed span gestalt, preference rule, and statistical learning perspectives; a diverse collection with a common thread-the centrality of 'expectations' to music cognition. We operationalize some recurrent themes across this collection as probabilistic descriptions of melodic tendency, codifying them as stochastic melody generation techniques. The techniques are combined into a concise melody generator, with salient parameters exposed for ready manipulation in real time. These techniques may be especially relevant to algorithmic composers, the live-coding community, and to music psychologists/theorists interested in how computational interpretations of cognitive models 'sound' in practice.
Computer Music Journal
© 2015 Massachusetts Institute of Technology. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.