Unsupervised music understanding based on nonparametric Bayesian models
Kazuyoshi Yoshii, Masataka Goto · 2012
This paper presents a new research framework for unsupervised music understanding. Our goal is to recognize musical notes from polyphonic audio signals and simultaneously induce grammatical patterns from the recognized notes by integrating probabilistic acoustic and language models. Given music audio signals, both models could be jointly trained in a self-organizing manner without manually specifying the numbers of musical notes and grammatical patterns. In this paper, we introduce our nonparametric Bayesian acoustic and language models for multipitch analysis and chord progression analysis and discuss issues for integrating these models. We then provide a novel overview of various acoustic and language models whose underlying concepts are useful for implementing the framework.