Abstract

An important problem in computational music analysis is the representation and automated discovery of recurrent patterns. In this paper we present a new method for pattern representation and discovery in a large corpus of music. Using the formalism of multiple viewpoints, music is viewed as multiple streams of description derived from the basic surface representation. Patterns are discovered within viewpoint sequences derived from the corpus for selected viewpoints. A statistical method is used to restrict attention to only those patterns which occur much more frequently than expected, where expectation is based on a Markov model of viewpoint elements. The concept of the longest significant patterns in a corpus is introduced. The method presented in this paper is designed to rapidly enumerate all longest significant patterns within a large corpus. An application of the method to the Bach chorales is presented.