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.

该论文提出了一种基于时间预测误差模型集成的无监督方法实现了符号音乐分割，该方法应用于 Essen Folksong 数据集上达到了最前沿的性能，其中使用了峰值检测算法选择段落候选，并聚合多个模型的预测达到最终的分割结果，但与有监督方法相比，该方法的表现仍有提升空间。

使用集成时间预测误差进行无监督符号音乐分段

Unsupervised Symbolic Music Segmentation using Ensemble Temporal Prediction Errors

We propose a self-supervised representation learning model for the task of
unsupervised phoneme boundary detection. The model is a convolutional neural
network that operates directly on the raw waveform. It is optimized to identify
spectral changes in the signal using the Noise-Contrastive Estimation
principle. At test time, a peak detection algorithm is applied over the model
outputs to produce the final boundaries. As such, the proposed model is trained
in a fully unsupervised manner with no manual annotations in the form of target
boundaries nor phonetic transcriptions. We compare the proposed approach to
several unsupervised baselines using both TIMIT and Buckeye corpora. Results
suggest that our approach surpasses the baseline models and reaches
state-of-the-art performance on both data sets. Furthermore, we experimented
with expanding the training set with additional examples from the Librispeech
corpus. We evaluated the resulting model on distributions and languages that
were not seen during the training phase (English, Hebrew and German) and showed
that utilizing additional untranscribed data is beneficial for model
performance.

本研究提出了一种自监督表示学习模型，用于无监督音素边界检测，该模型是一个卷积神经网络，直接在原始波形上进行操作，利用噪声对比估计原则优化识别信号中的频谱变化，并在测试时间通过峰值检测算法应用于模型输出以产生最终边界。结果表明，该方法优于基线模型，并在两个数据集上达到了最先进的性能，同时使用额外未转录数据有助于模型表现。