Techniques for Generative Melodies Inspired by Music Cognition

Abstract

This article 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; this is 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 and theorists interested in how computational interpretations of cognitive models “sound” in practice.