Paper ID: 2112.08561

EmotionBox: a music-element-driven emotional music generation system using Recurrent Neural Network

Kaitong Zheng, Ruijie Meng, Chengshi Zheng, Xiaodong Li, Jinqiu Sang, Juanjuan Cai, Jie Wang

With the development of deep neural networks, automatic music composition has made great progress. Although emotional music can evoke listeners' different emotions and it is important for artistic expression, only few researches have focused on generating emotional music. This paper presents EmotionBox -an music-element-driven emotional music generator that is capable of composing music given a specific emotion, where this model does not require a music dataset labeled with emotions. Instead, pitch histogram and note density are extracted as features that represent mode and tempo respectively to control music emotions. The subjective listening tests show that the Emotionbox has a more competitive and balanced performance in arousing a specified emotion than the emotion-label-based method.

Submitted: Dec 16, 2021