Improving Motif Discovery of Symbolic Polyphonic Music with Motif Note Identification

Motif discovery in polyphonic symbolic music data is an important yet challenging task in music processing. In this paper, we propose a novel motif-discovery method created by combining the traditional rule-based repeated pattern discovery algorithms with a machine learning–based model that performs the task of motif note identification, i.e., identifying whether or not a note belongs to a motif. More specifically, the motif note identification model extracts motif notes for subsequent repeated pattern discovery. Removing non-motif notes can reduce the unwanted outputs in repeated pattern discovery and thereby improve performance. With a limited amount of training data, motif note identification can be implemented by fine-tuning a pre-trained model for symbolic music using pseudo-labels. The results demonstrate the feasibility of applying data-driven methods to assist the motif-discovery task, specifically on the occurrence and three-layer metrics, under the situation that labeled training data of the motif and repeated pattern are scarce.