Composing musical ideas longer than motifs or figures is still rare in music generated by machine learning methods, a problem that is commonly referred to as the lack of long-term structure in the generated sequences. In addition, the evaluation of the structural complexity of artificial compositions is still a manual task, requiring expert knowledge, time and involving subjectivity which is inherent in the perception of musical structure. Based on recent advancements in music structure analysis, we automate the evaluation process by introducing a collection of metrics that can objectively describe structural properties of the music signal. This is done by segmenting music hierarchically, and computing our metrics on the resulting hierarchies to characterise the decomposition process of music into its structural components. We tested our method on a dataset collecting music with different degrees of structural complexity, from random and computer-generated pieces to real compositions of different genres and formats. Results indicate that our method can discriminate between these classes of complexity and identify further non-trivial subdivisions according to their structural properties. Our work contributes a simple yet effective framework for the evaluation of music generation models in regard to their ability to create structurally meaningful compositions.

中文翻译：

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测量音乐的结构复杂性：从结构分割到音乐生成模型的自动评估


在机器学习方法生成的音乐中，创作比主题或图形更长的音乐思想仍然很少见，这个问题通常被称为生成的序列中缺乏长期结构。此外，评估人工作品的结构复杂性仍然是一项手动任务，需要专业知识、时间并涉及音乐结构感知中固有的主观性。基于音乐结构分析的最新进展，我们通过引入一系列可以客观描述音乐信号结构特性的指标来自动化评估过程。这是通过对音乐进行分层划分，并计算所得分层结构的指标来描述音乐分解为其结构组件的过程来完成的。我们在收集具有不同结构复杂程度的音乐的数据集上测试了我们的方法，从随机和计算机生成的作品到不同流派和格式的真实作品。结果表明，我们的方法可以区分这些复杂类别，并根据其结构特性识别进一步的重要细分。我们的工作为评估音乐生成模型创建具有结构意义的作品的能力提供了一个简单而有效的框架。