Unsupervised Melody Style Conversion

Abstract

We study a method for converting the music style of a given melody to a target style (e.g. from classical music style to pop music style) based on unsupervised statistical learning. Following the analogy with machine translation, we propose a statistical formulation of style conversion based on integration of a music language model of the target style and an edit model representing the similarity between the original and arranged melodies. In supervised-learning approaches for constructing style-specific language models, it has been crucial to use data that properly specify a music style. To reduce reliance on manual data selection and annotation, we propose a novel statistical model that can spontaneously discover styles in pitch and rhythm organization. We also point out the importance of an edit model that incorporates syntactic functions of notes such as tonic and build a model that can infer such functions unsupervisedly. We confirm that the proposed method improves the quality of arrangement by examining the results and by subjective evaluation.