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Music structure analysis (MSA) systems aim to segment a song recording intonon-overlapping sections with useful labels. Previous MSA systems typicallypredict abstract labels in a post-processing step and require the full contextof the song. By contrast, we recently proposed a supervised framework, called"Music Structural Function Analysis" (MuSFA), that models and predictsmeaningful labels like 'verse' and 'chorus' directly from audio, withoutrequiring the full context of a song. However, the performance of this systemdepends on the amount and quality of training data. In this paper, we proposeto repurpose a public dataset, HookTheory Lead Sheet Dataset (HLSD), to improvethe performance. HLSD contains over 18K excerpts of music sections originallycollected for studying automatic melody harmonization. We treat each excerpt asa partially labeled song and provide a label mapping, so that HLSD can be usedtogether with other public datasets, such as SALAMI, RWC, and Isophonics. Incross-dataset evaluations, we find that including HLSD in training can improvestate-of-the-art boundary detection and section labeling scores by ~3% and ~1%respectively.

MuSFA: Improving Music Structural Function Analysis with Partially Labeled Data