Intra-opus repeated pattern discovery in polyphonic symbolic music data has challenges in both algorithm design and data annotation. To solve these challenges, we propose BPS-motif, a new symbolic music dataset containing the note-level annotation of motives and occurrences in Beethoven's piano sonatas. The size of the proposed dataset is larger than previous symbolic datasets for repeated pattern discovery. We report the process of dataset annotation, specifically a peer review process and discussion phase to improve the annotation quality. Finally, we propose a motif discovery method which is shown outperforming baseline methods on repeated pattern discovery.

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