  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.
