AURA：面向对话式音乐编辑的统一多模态框架

Computer Science > Sound arXiv:2609.14344 (cs) [Submitted on 13 Sep 2026] Title:AURA: Unified Multimodal Framework for Conversational Music Editing Authors:Quoc-Huy Trinh, Minh-Van Nguyen, Debesh Jha View a PDF of the paper titled AURA: Unified Multimodal Framework for Conversational Music Editing, by Quoc-Huy Trinh and 2 other authors View PDF HTML (experimental) Abstract:Instruction-guided music editors typically process each request independently, limiting their ability to support workflows in which users progressively refine a track. We introduce AURA, a unified multimodal framework for conversational music editing. AURA uses a multimodal large language model to interpret the complete dialogue history, an optional image, and reference audio, distilling the editing intent into compact concept tokens. A concept-to-audio module injects these tokens and frame-aligned reference features into a frozen MusicGen backbone, enabling precise edits while preserving unaffected content. AURA optimizes only 91M parameters while retaining 1.9B frozen backbone parameters. Experiments on Slakh2100 and MoisesDB demonstrate substantial improvements in edit correctness and content preservation over existing instruction-guided methods, including a 4-5 times reduction in FAD for out-of-domain addition and removal. Subjects: Sound (cs.SD); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS) Cite as: arXiv:2609.14344 [cs.SD] (or arXiv:2609.14344v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2609.14344 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Quoc-Huy Trinh [view email] [v1] Sun, 13 Sep 2026 07:09:30 UTC (101 KB) Full-text links: Access Paper: View a PDF of the paper titled AURA: Unified Multimodal Framework for Conversational Music Editing, by Quoc-Huy Trinh and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SD new | recent | 2026-09 Change to browse by: cs cs.AI eess eess.AS References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)