Title: EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template Show Chinese title

Title: EMelodyGen: 基于ABC记谱法且使用音乐特征模板的情感条件旋律生成

Abstract: The EMelodyGen system focuses on emotional melody generation in ABC notation controlled by the musical feature template. Owing to the scarcity of well-structured and emotionally labeled sheet music, we designed a template for controlling emotional melody generation by statistical correlations between musical features and emotion labels derived from small-scale emotional symbolic music datasets and music psychology conclusions. We then automatically annotated a large, well-structured sheet music collection with rough emotional labels by the template, converted them into ABC notation, and reduced label imbalance by data augmentation, resulting in a dataset named Rough4Q. Our system backbone pre-trained on Rough4Q can achieve up to 99% music21 parsing rate and melodies generated by our template can lead to a 91% alignment on emotional expressions in blind listening tests. Ablation studies further validated the effectiveness of the feature controls in the template. Available code and demos are at https://github.com/monetjoe/EMelodyGen. Show Chinese abstract

Abstract: EMelodyGen系统专注于基于ABC记谱法的情感旋律生成，受音乐特征模板控制。由于结构良好且带有情感标签的乐谱稀缺，我们设计了一个模板，通过从小型情感符号音乐数据集和音乐心理学结论中推导出的音乐特征与情感标签之间的统计相关性来控制情感旋律生成。然后，我们使用该模板自动为一个大型、结构良好的乐谱集合添加粗略的情感标签，将其转换为ABC记谱法，并通过数据增强减少标签不平衡，从而创建了一个名为Rough4Q的数据集。我们的系统主干在Rough4Q上预训练后可以达到高达99%的music21解析率，而由我们的模板生成的旋律在盲听测试中可以在情感表达方面达到91%的一致性。消融研究进一步验证了模板中特征控制的有效性。可在https://github.com/monetjoe/EMelodyGen获取代码和演示。