  Symbolic music segmentation is the process of dividing symbolic melodies into
smaller meaningful groups, such as melodic phrases. We proposed an unsupervised
method for segmenting symbolic music. The proposed model is based on an
ensemble of temporal prediction error models. During training, each model
predicts the next token to identify musical phrase changes. While at test time,
we perform a peak detection algorithm to select segment candidates. Finally, we
aggregate the predictions of each of the models participating in the ensemble
to predict the final segmentation. Results suggest the proposed method reaches
state-of-the-art performance on the Essen Folksong dataset under the
unsupervised setting when considering F-Score and R-value. We additionally
provide an ablation study to better assess the contribution of each of the
model components to the final results. As expected, the proposed method is
inferior to the supervised setting, which leaves room for improvement in future
research considering closing the gap between unsupervised and supervised
methods.
