用于符号旋律分析的计算特征
Computational Features for Symbolic Melody Analysis
- University of Cambridge（剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出开源Python工具melody-features，梳理符号旋律的音乐理论与心理学特征并构建分类体系，在Essen民歌集上验证其风格分类性能，为旋律分析提供可解释的特征方案。
AI中文摘要：
本文解决了从符号编码旋律中提取音乐理论与心理学特征的通用问题。我们梳理了现有旋律特征提取工具箱，枚举其特征并将其组织为统一分类体系。随后描述了一款新软件库，它在简洁的Python包中实现了所有这些特征。我们在Essen FolkSong Collection数据集上验证了组合特征集，利用该数据集构建了一系列风格分类模型，这些模型帮助我们解答关于特征集可解释性与维度的关键问题。结果显示，使用完整特征集可达到优异的分类准确率，而8维因子分析解决方案表现出良好性能，提升了分类器的可解释性。我们将这款名为melody-features的新工具箱作为开源Python包发布，可便捷应用于音乐分析、音乐心理学与音乐信息检索等各类场景。
英文摘要：
This paper addresses the general problem of extracting music-theoretic and psychological features from symbolically encoded melodies. We review existing melodic feature extraction toolboxes, enumerate their features, and organise them into a common taxonomy. We then describe a new software library that provides implementations of all of these features in a straightforward Python package. We then demonstrate the combined feature set on the Essen Folksong Collection, using the dataset to produce a series of style classification models. These models help us answer key questions about the interpretability and dimensionality of the feature set. Our results show excellent classification accuracy using the full feature set, and promising performance for an eight-dimensional factor-analytic solution that improves the interpretability of the classifier. We distribute our new toolbox as an open-source Python package, $\mathtt{melody-features}$, which can easily be used in various applications within music analysis, music psychology, and music information retrieval.
