MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data

TL;DR

This paper enhances music structural analysis by leveraging a large, partially labeled dataset (HLSD) to improve boundary detection and section labeling accuracy in a direct prediction framework.

Contribution

It repurposes the HLSD dataset for music structure analysis, demonstrating improved performance by combining it with existing datasets in a partially labeled training approach.

Findings

Improved boundary detection scores by ~3%.

Enhanced section labeling accuracy by ~1%.

Effective use of partially labeled data for music analysis.

Abstract

Music structure analysis (MSA) systems aim to segment a song recording into non-overlapping sections with useful labels. Previous MSA systems typically predict abstract labels in a post-processing step and require the full context of the song. By contrast, we recently proposed a supervised framework, called "Music Structural Function Analysis" (MuSFA), that models and predicts meaningful labels like 'verse' and 'chorus' directly from audio, without requiring the full context of a song. However, the performance of this system depends on the amount and quality of training data. In this paper, we propose to repurpose a public dataset, HookTheory Lead Sheet Dataset (HLSD), to improve the performance. HLSD contains over 18K excerpts of music sections originally collected for studying automatic melody harmonization. We treat each excerpt as a partially labeled song and provide a label mapping, so that HLSD can be used together with other public datasets, such as SALAMI, RWC, and Isophonics. In cross-dataset evaluations, we find that including HLSD in training can improve state-of-the-art boundary detection and section labeling scores by ~3% and ~1% respectively.