# EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emotions

arXiv: https://arxiv.org/abs/2509.15986 · HTML: https://mlsanity.com/paper/2509.15986/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 29 references that are in the map.
- First posted on arXiv 19 Sep 2025.

## Builds on (most cited first, 16 of 16)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [MusicLM: Generating Music From Text](https://mlsanity.com/paper/2301.11325/) (2023)
- [MuLan: A Joint Embedding of Music Audio and Natural Language](https://mlsanity.com/paper/2208.12415/) (2022)
- [WavJourney: Compositional Audio Creation with Large Language Models](https://mlsanity.com/paper/2307.14335/) (2023)
- [CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages](https://mlsanity.com/paper/2502.10362/) (2025)
- [WavCraft: Audio Editing and Generation with Large Language Models](https://mlsanity.com/paper/2403.09527/) (2024)
- [DExter: Learning and Controlling Performance Expression with Diffusion Models](https://mlsanity.com/paper/2406.14850/) (2024)
- [Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study](https://mlsanity.com/paper/2303.03857/) (2023)
- [Adapting Language-Audio Models as Few-Shot Audio Learners](https://mlsanity.com/paper/2305.17719/) (2023)
- [Acoustic Prompt Tuning: Empowering Large Language Models with Audition Capabilities](https://mlsanity.com/paper/2312.00249/) (2023)
- [Bridging Paintings and Music -- Exploring Emotion based Music Generation through Paintings](https://mlsanity.com/paper/2409.07827/) (2024)
- [Bridging Discrete and Continuous: A Multimodal Strategy for Complex Emotion Detection](https://mlsanity.com/paper/2409.07901/) (2024)
- [LLaQo: Towards a Query-Based Coach in Expressive Music Performance Assessment](https://mlsanity.com/paper/2409.08795/) (2024)
- [RenderBox: Expressive Performance Rendering with Text Control](https://mlsanity.com/paper/2502.07711/) (2025)

## Abstract (arXiv)

Existing digital mental wellness tools often overlook the nuanced emotional states underlying everyday challenges. For example, pre-sleep anxiety affects more than 1.5 billion people worldwide, yet current approaches remain largely static and "one-size-fits-all", failing to adapt to individual needs. In this work, we present EmoHeal, an end-to-end system that delivers personalized, three-stage supportive narratives. EmoHeal detects 27 fine-grained emotions from user text with a fine-tuned XLM-RoBERTa model, mapping them to musical parameters via a knowledge graph grounded in music therapy principles (GEMS, iso-principle). EmoHeal retrieves audiovisual content using the CLAMP3 model to guide users from their current state toward a calmer one ("match-guide-target"). A within-subjects study (N=40) demonstrated significant supportive effects, with participants reporting substantial mood improvement (M=4.12, p<0.001) and high perceived emotion recognition accuracy (M=4.05, p<0.001). A strong correlation between perceived accuracy and therapeutic outcome (r=0.72, p<0.001) validates our fine-grained approach. These findings establish the viability of theory-driven, emotion-aware digital wellness tools and provides a scalable AI blueprint for operationalizing music therapy principles.