arXiv (All)
open access
How Well Do Generative Music Models Follow Emotion Conditioning?
Recent generative music models offer increasingly fine-grained control through text and audio conditioning, yet how faithfully they follow intended emotional cues remains an open question. We address this gap with a unified evaluation pipeline for emotion-following in generated music. Using all 1000
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- How Well Do Generative Music Models Follow Emotion Conditioning?#797810
- UniSonate: A Unified Model for Speech, Music, and Sound Effect Generation with Text Instructions#134610
- LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training#321861
- Composer2Vec: A Continuous Embedding Space of Composer Style Learned from Symbolic Melody Generation#1034867
- Do Music Generative Models Understand Musical Qualities? Automatic Music Evaluation with Model-Intrinsic Signals#1142022
Record · ID 799233
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive