{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/openmu-your-swiss-army-knife-for-music","title":"OpenMU: Your Swiss Army Knife for Music Understanding","arxiv_id":"2410.15573","date":"2024-10-21","proceeding":null,"authors":["Mengjie Zhao","Zhi Zhong","Zhuoyuan Mao","Shiqi Yang","Wei-Hsiang Liao","Shusuke Takahashi","Hiromi Wakaki","Yuki Mitsufuji"],"abstract":"We present OpenMU-Bench, a large-scale benchmark suite for addressing the data scarcity issue in training multimodal language models to understand music. To construct OpenMU-Bench, we leveraged existing datasets and bootstrapped new annotations. OpenMU-Bench also broadens the scope of music understanding by including lyrics understanding and music tool usage. Using OpenMU-Bench, we trained our music understanding model, OpenMU, with extensive ablations, demonstrating that OpenMU outperforms baseline models such as MU-Llama. Both OpenMU and OpenMU-Bench are open-sourced to facilitate future research in music understanding and to enhance creative music production efficiency.","url_abs":"https://arxiv.org/abs/2410.15573v3","url_pdf":"https://arxiv.org/pdf/2410.15573v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"openmu-your-swiss-army-knife-for-music","repo_url":"https://github.com/mzhaojp22/openmu","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"openmu-your-swiss-army-knife-for-music","repo_url":"https://github.com/sony/openmu","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2410.15573","atlas_url":"https://app.syntology.ai/?focus=2410.15573","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}