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

arXiv: https://arxiv.org/abs/2112.08561 · HTML: https://mlsanity.com/paper/2112.08561/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2021.

## Built on by (most cited first, 2 of 2)

- [AI-Based Affective Music Generation Systems: A Review of Methods, and Challenges](https://mlsanity.com/paper/2301.06890/) (2023)
- [EmoGen: Eliminating Subjective Bias in Emotional Music Generation](https://mlsanity.com/paper/2307.01229/) (2023)

## Abstract (arXiv)

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.