# Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation

arXiv: https://arxiv.org/abs/2407.20955 · HTML: https://mlsanity.com/paper/2407.20955/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 51 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs](https://mlsanity.com/paper/2101.02402/) (2021)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [A Theoretical Analysis of the Repetition Problem in Text Generation](https://mlsanity.com/paper/2012.14660/) (2020)
- [EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation](https://mlsanity.com/paper/2108.01374/) (2021)
- [MuseCoco: Generating Symbolic Music from Text](https://mlsanity.com/paper/2306.00110/) (2023)
- [MuseMorphose: Full-Song and Fine-Grained Piano Music Style Transfer with One Transformer VAE](https://mlsanity.com/paper/2105.04090/) (2021)
- [Music transcription modelling and composition using deep learning](https://mlsanity.com/paper/1604.08723/) (2016)
- [Learning to Generate Music With Sentiment](https://mlsanity.com/paper/2103.06125/) (2021)
- [Music SketchNet: Controllable Music Generation via Factorized Representations of Pitch and Rhythm](https://mlsanity.com/paper/2008.01291/) (2020)
- [Melody transcription via generative pre-training](https://mlsanity.com/paper/2212.01884/) (2022)
- [Computer-Generated Music for Tabletop Role-Playing Games](https://mlsanity.com/paper/2008.07009/) (2020)
- [Automatic Melody Harmonization with Triad Chords: A Comparative Study](https://mlsanity.com/paper/2001.02360/) (2020)
- [Compose & Embellish: Well-Structured Piano Performance Generation via A Two-Stage Approach](https://mlsanity.com/paper/2209.08212/) (2022)
- [Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search](https://mlsanity.com/paper/2208.05162/) (2022)
- [Generating music with sentiment using Transformer-GANs](https://mlsanity.com/paper/2212.11134/) (2022)
- [A data-driven approach to mid-level perceptual musical feature modeling](https://mlsanity.com/paper/1806.04903/) (2018)
- [YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations](https://mlsanity.com/paper/2211.07131/) (2022)
- [Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features](https://mlsanity.com/paper/2307.03544/) (2023)
- [EmoGen: Eliminating Subjective Bias in Emotional Music Generation](https://mlsanity.com/paper/2307.01229/) (2023)
- [On Perceived Emotion in Expressive Piano Performance: Further Experimental Evidence for the Relevance of Mid-level Perceptual Features](https://mlsanity.com/paper/2107.13231/) (2021)
- [MoodLoopGP: Generating Emotion-Conditioned Loop Tablature Music with Multi-Granular Features](https://mlsanity.com/paper/2401.12656/) (2024)
- [Emotion-Driven Melody Harmonization via Melodic Variation and Functional Representation](https://mlsanity.com/paper/2407.20176/) (2024)
- [An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation](https://mlsanity.com/paper/2301.13383/) (2023)
- [MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion](https://mlsanity.com/paper/2312.10307/) (2023)

## Built on by (most cited first, 5 of 5)

- [Time-Shifted Token Scheduling for Symbolic Music Generation](https://mlsanity.com/paper/2509.23749/) (2025)
- [From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training](https://mlsanity.com/paper/2506.17497/) (2025)
- [MuseTok: Symbolic Music Tokenization for Generation and Semantic Understanding](https://mlsanity.com/paper/2510.16273/) (2025)
- [AImoclips: A Benchmark for Evaluating Emotion Conveyance in Text-to-Music Generation](https://mlsanity.com/paper/2509.00813/) (2025)
- [Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generation](https://mlsanity.com/paper/2608.03999/) (2026)

## Abstract (arXiv)

Managing the emotional aspect remains a challenge in automatic music generation. Prior works aim to learn various emotions at once, leading to inadequate modeling. This paper explores the disentanglement of emotions in piano performance generation through a two-stage framework. The first stage focuses on valence modeling of lead sheet, and the second stage addresses arousal modeling by introducing performance-level attributes. To further capture features that shape valence, an aspect less explored by previous approaches, we introduce a novel functional representation of symbolic music. This representation aims to capture the emotional impact of major-minor tonality, as well as the interactions among notes, chords, and key signatures. Objective and subjective experiments validate the effectiveness of our framework in both emotional valence and arousal modeling. We further leverage our framework in a novel application of emotional controls, showing a broad potential in emotion-driven music generation.