Generalized musical pattern discovery by analogy from local viewpoints

Abstract

Musical knowledge discovery, an important issue of digital network processing, is also a crucial question for music. Indeed, music may be considered as a kind of network. A new approach for Musical Pattern Discovery is proposed, which tries to consider musical discourse in a general polyphonic framework. We suggest a new vision of automated pattern analysis that generalizes the multiple viewpoint approach. Sharing the idea that pattern emerges from repetition, analogy-based modeling of music understanding adds the idea of a permanent induction of global hypotheses from local perception. Through a chronological scanning of the score, analogies are inferred between local relationships – namely, notes and intervals – and global structures – namely, patterns – whose paradigms are stored inside an abstract pattern trie. Basic mechanisms for inference of new patterns are described. Such an elastic vision of music enables a generalized understanding of its plastic expression.