Emotion-driven melody harmonization aims to generate diverse harmonies for a single melody to convey desired emotions. Previous research found it hard to alter the perceived emotional valence of lead sheets only by harmonizing the same melody with different chords, which may be attributed to the constraints imposed by the melody itself and the limitation of existing music representation. In this paper, we propose a novel functional representation for symbolic music. This new method takes musical keys into account, recognizing their significant role in shaping music's emotional character through major-minor tonality. It also allows for melodic variation with respect to keys and addresses the problem of data scarcity for better emotion modeling. A Transformer is employed to harmonize key-adaptable melodies, allowing for keys determined in rule-based or model-based manner. Experimental results confirm the effectiveness of our new representation in generating key-aware harmonies, with objective and subjective evaluations affirming the potential of our approach to convey specific valence for versatile melody.

翻译：情感驱动的旋律和声化旨在为单一旋律生成多样化的和声，以传达期望的情感。先前研究发现，仅通过为同一旋律配以不同和弦难以改变主旋律谱的情感效价感知，这可能归因于旋律本身的约束以及现有音乐表征的局限性。本文提出了一种新颖的符号音乐功能表征方法。该新方法将音乐调性纳入考量，认识到其通过大小调体系在塑造音乐情感特质中的重要作用。该方法还支持基于调性的旋律变奏，并通过缓解数据稀缺问题以提升情感建模效果。我们采用Transformer模型对可适应调性的旋律进行和声化处理，支持通过基于规则或基于模型的方式确定调性。实验结果证实了我们新表征方法在生成调性感知和声方面的有效性，主客观评估均验证了本方法在传达特定情感效价以适配多样化旋律方面的潜力。