Melody is one of the most important components in music. Unlike other components in music theory, such as harmony and counterpoint, computable features for melody is urgently in need. These features are highly demanded as data-driven methods dominating the fields such as musical information retrieval and automatic music composition. To boost the performance of deep-learning-related musical tasks, we propose a set of interpretable features on monophonic melody for computational purposes. These features are defined not only in mathematical form, but also with some considerations on composers 'intuition. For example, the Melodic Center of Gravity can reflect the sentence-wise contour of the melody, the local / global melody dynamics quantifies the dynamics of a melody that couples pitch and time in a sentence. We found that these features are considered by people universally in many genres of songs, even for atonal composition practices. Hopefully, these melodic features can provide nov el inspiration for future researchers as a tool in the field of MIR and automatic composition.

中文翻译：

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探索歌曲单音旋律的内在特性

旋律是音乐中最重要的组成部分之一。与音乐理论中的其他组成部分（例如和声和对位）不同，旋律的可计算特征是迫切需要的。作为主导音乐信息检索和自动音乐创作等领域的数据驱动方法，这些功能的需求量很大。为了提高与深度学习相关的音乐任务的性能，我们提出了一组用于计算目的的单音旋律的可解释特征。这些特征不仅以数学形式定义，而且还考虑了作曲家的直觉。例如，旋律重心可以反映旋律的句子轮廓，局部/全局旋律动态量化了在句子中结合音高和时间的旋律动态。我们发现，人们普遍认为这些特征在许多类型的歌曲中都是普遍存在的，即使是在无调性作曲实践中也是如此。希望这些旋律特征可以为未来的研究人员作为 MIR 和自动组合领域的工具提供新的灵感。