论文标题
自我监督的层次结构建模
Self-Supervised Hierarchical Metrical Structure Modeling
论文作者
论文摘要
我们提出了一种新颖的方法，可以以最小的领域知识为符号音乐和音频信号建模符号音乐和音频信号。该模型在节拍一致的音乐信号上训练和推断，并预测了从节拍，测量到部分级别的8层层次级别树。训练程序不需要任何分层的半标记，除了BEATS，纯粹依赖于度量规则性的性质和作为归纳性偏见的元音一致性的性质。我们在实验中表明，该方法在符号音乐和音频信号的多个度量结构分析任务上，与受监督的基线相当的性能。所有演示，源代码和预培训模型均在GitHub上公开可用。
We propose a novel method to model hierarchical metrical structures for both symbolic music and audio signals in a self-supervised manner with minimal domain knowledge. The model trains and inferences on beat-aligned music signals and predicts an 8-layer hierarchical metrical tree from beat, measure to the section level. The training procedure does not require any hierarchical metrical labeling except for beats, purely relying on the nature of metrical regularity and inter-voice consistency as inductive biases. We show in experiments that the method achieves comparable performance with supervised baselines on multiple metrical structure analysis tasks on both symbolic music and audio signals. All demos, source code and pre-trained models are publicly available on GitHub.
