_:n00373e576bf14f13a3f2db32b9907694xb33
<http://schema.org/description>
"Symbolic music segmentation is the process of dividing symbolic melodies into\nsmaller meaningful groups, such as melodic phrases. We proposed an unsupervised\nmethod for segmenting symbolic music. The proposed model is based on an\nensemble of temporal prediction error models. During training, each model\npredicts the next token to identify musical phrase changes. While at test time,\nwe perform a peak detection algorithm to select segment candidates. Finally, we\naggregate the predictions of each of the models participating in the ensemble\nto predict the final segmentation. Results suggest the proposed method reaches\nstate-of-the-art performance on the Essen Folksong dataset under the\nunsupervised setting when considering F-Score and R-value. We additionally\nprovide an ablation study to better assess the contribution of each of the\nmodel components to the final results. As expected, the proposed method is\ninferior to the supervised setting, which leaves room for improvement in future\nresearch considering closing the gap between unsupervised and supervised\nmethods."
<https://cdnjs.deepai.org/profile/jeffrey-s-rosenschein>