Musical Similarity and Commonness Estimation Based on Probabilistic Generative Models

Abstract

This paper proposes a novel concept we call musical commonness, which is the similarity of a song to a set of songs, in other words, its typicality. This commonness can be used to retrieve representative songs from a song set (e.g., songs released in the 80s or 90s). Previous research on musical similarity has compared two songs but has not evaluated the similarity of a song to a set of songs. The methods presented here for estimating the similarity and commonness of polyphonic musical audio signals are based on a unified framework of probabilistic generative modeling of four musical elements (vocal timbre, musical timbre, rhythm, and chord progression). To estimate the commonness, we use a generative model trained from a song set instead of estimating musical similarities of all possible song-pairs by using a model trained from each song. In experimental evaluation, we used 3278 popular music songs. Estimated song-pair similarities are comparable to ratings by a musician at the 0.1% significance level for vocal and musical timbre, at the 1% level for rhythm, and the 5% level for chord progression. Results of commonness evaluation show that the higher the musical commonness is, the more similar a song is to songs of a song set.