Unsupervised grammar induction from music data

Abstract

Music has a lot of similarities to language. Since most languages have clear syntactic structures (e.g., words should be arranged in the SVO order), many linguists have proposed various kinds of grammar theories by carefully and manually investigating language data. The situation is the same with Western music. Although a single musical note (cf. alphabet) has no meaning by itself, a cluster or pattern of multiple musical notes over the quantized time-frequency grid (cf. word) can invoke some impression and such short patterns are concatenated or superimposed (unique to music) to produce more complicated meaning (cf. sentence). We introduce several attempts to discover latent structures underlying music from acoustic or symbolic data (music signals and musical scores) in an unsupervised manner. Integrating statistical acoustic and language models as in speech recognition, for example, it is possible not only to transcribe music but also to discover that particular note combinations can form chords. A key feature of this approach is that both models are trained jointly only from acoustic data. Recently, we have attempted to induce music grammars from polyphonic scores by leveraging the state-of-the-art techniques of natural language processing. This would contribute to automatic music transcription and computational musicology.