构建可计算的旋律分析工具包，助力音乐风格识别与心理机制研究
Computational Features for Symbolic Melody Analysis
- 统一整理音乐理论与心理特征，形成标准化分类体系
- 在埃森民歌集上实现高精度风格分类，8维因子解具可解释性
- 开源Python库支持音乐分析、心理与信息检索多场景应用
本文针对符号化旋律中提取音乐理论与心理特征的通用问题展开研究。系统回顾现有旋律特征提取工具箱，梳理其功能并建立统一分类框架。随后开发一个新软件库，以简洁的Python接口实现所有特征。在埃森民歌集（Essen Folksong Collection）上验证特征集性能，构建一系列风格分类模型。结果表明，完整特征集达到优异分类准确率；八维因子分析解在保持良好性能的同时显著提升分类器可解释性。本文将工具包以开源形式发布，命名为 $\mathtt{melody-features}$，便于在音乐分析、音乐心理学与音乐信息检索等领域直接使用。
原文摘要 · Abstract (English)
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.
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