{"ID":2868844,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15986","arxiv_id":"2509.15986","title":"EmoHeal: An End-to-End System for Personalized Therapeutic Music Retrieval from Fine-grained Emotions","abstract":"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\u003c0.001) and high perceived emotion recognition accuracy (M=4.05, p\u003c0.001). A strong correlation between perceived accuracy and therapeutic outcome (r=0.72, p\u003c0.001) 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.","short_abstract":"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 p...","url_abs":"https://arxiv.org/abs/2509.15986","url_pdf":"https://arxiv.org/pdf/2509.15986v1","authors":"[\"Xinchen Wan\",\"Jinhua Liang\",\"Huan Zhang\"]","published":"2025-09-19T13:52:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"cs.CL\",\"cs.HC\",\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}