arXiv (All)
open access
Melody Guidance for Audio Diffusion Models Without Retraining
Controlling melody in audio generation normally requires training a conditional generative model on audio paired with pitch curves or MIDI notes. We instead guide an existing unconditional diffusion model, originally trained without melody inputs, to follow a target melody without retraining or modi
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
- Composer2Vec: A Continuous Embedding Space of Composer Style Learned from Symbolic Melody Generation#1034867
- LeVo 2: Stable and Melodious Song Generation via Hierarchical Representation Modeling and Progressive Post-Training#321861
- SongCraft: Unified Song Generation and Editing with Reconstructive Learning#1110680
- DriftAudio: Marginal Drifting for Distributional Post-Training of One-Step Text-to-Audio Generators#1064142
- Time-Varying Audio Effect Modeling by End-to-End Adversarial Training#1328921
Record · ID 1447578
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