[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-86147-en":3,"doc-seo-86147-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},86147,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","BeatEdit Symbolic Music Generation as Explicit Editing","Music creation is a revision process, yet symbolic music generation is still dominated by systems that generate full sequences from scratch and offer limited control for targeted changes. Existing edit-based ideas in NLP support explicit keep/delete/replace/insert operations, but symbolic music lacks representations with the required structural properties for explicit editing. BeatEdit introduces a beat-grid anchored encoding and a three-stage edit framework, improving precision, perceptual quality, and efficiency with single-pass inference under 100 ms, confirmed by cross-encoding evaluations.","BeatEdit: Symbolic Music Generation as Explicit Editing  \nHaoyu Gu  \n[ghy20050104@gmail.com](ghy20050104@gmail.com)[ ](ghy20050104@gmail.com)School of Future Technology South China University of Technology Guangzhou, China  \nLekai Qian  \n[202364870191@mail.scut.edu.cn](202364870191@mail.scut.edu.cn)[ ](202364870191@mail.scut.edu.cn)School of Future Technology South China University of Technology Guangzhou, China  \nHaowu Zhou  \n[202364870491@mail.scut.edu.cn](202364870491@mail.scut.edu.cn)[ ](202364870491@mail.scut.edu.cn)School of Future Technology South China University of Technology Guangzhou, China  \nQi Liu∗ [drliuqi@scut.edu.cn](drliuqi@scut.edu.cn)[ ](drliuqi@scut.edu.cn)School of Future Technology South China University of Technology Guangzhou, China  \nShuai Wang∗ [shuaiwang@nju.edu.cn](shuaiwang@nju.edu.cn)[ ](shuaiwang@nju.edu.cn)School of Intelligence Science and Technology Nanjing University Suzhou, China  \narXiv :2607 . 1 1 124v 1 [ cs . SD] 13 Jul 2026  \nAbstract  \nMusic creation is fundamentally a process of revision. Yet symbolic music generation remains dominated by paradigms that produce complete sequences from scratch, with limited support for selective modification. Edit-based methods have proven effective for text transformation tasks, but remain largely unexplored for symbolic music. We trace this absence to the representational level: conventional event-based music encodings lack the structural properties required by explicit music editing. In contrast, the Beat encoding, a beat-grid-anchored representation originally designed for autoregressive generation, possesses structural properties amenable to editing. We propose BeatEdit, the first framework for symbolic music generation based on explicit edit operations, recasting generation as producing new content by editing a draft rather than synthesizing from scratch. BeatEdit comprises three complementary mechanisms along an axis of increasing edit density: per-token sequence tagging for error correction, iterative refinement for accompaniment editing, and tag-then-fill for segment completion. All these mechanisms share a single encoding and pretrained backbone, achieving higher precision and perceptual quality than autoregressive and diffusion methods across all three tasks, while remaining efficient, with single-pass inference completing in under 100 ms. Cross-encoding evaluation further reveals that encoding design substantially influences editing effectiveness, with notable encoding–method interaction effects. Code is available at [https://github.com/Haoyu-Gu/BeatEdit-code](https://github.com/Haoyu-Gu/BeatEdit-code).  \nCCS Concepts  \n• Applied computing → Sound and music computing; • Computing methodologies → Natural language generation.  \nKeywords  \nsymbolic music generation, edit-based generation, music representation, music tokenization, non-autoregressive generation  \n∗ Corresponding authors.  \n1 Introduction  \nIn music production, much of the creative effort lies in refining what already exists rather than writing from scratch. A performer corrects wrong notes, an arranger reshapes accompaniment without altering the melody, and a composer fills in missing bars while preserving surrounding material. Despite their diversity, these tasks share a common nature: input and output overlap substantially, and the process itself is one of locating what needs change and applying targeted modifications, not regenerating the whole.  \nSymbolic music generation is currently dominated by two paradigms. Autoregressive Transformers [13, 18, 27, 33] model music as leftto-right event sequences, while diffusion models [14, 23] operate over grid-like representations through iterative denoising. Neither provides native support for selective modification: autoregressive generation must regenerate from the edit point onward even for a single-note change, and diffusion does not explicitly model which positions require change. Several works attempt local modification atop the","cbCaisoiRoplRaow","https://ap.wps.com/l/cbCaisoiRoplRaow","pdf",1143444,3,1,19,"English","en",105,"# Introduction\n## Music editing as revision\n## Limits of autoregressive and diffusion paradigms\n## Edit-based methods in NLP and the open question\n## Edit density spectrum and unified framework","[{\"question\":\"What limitation does BeatEdit address in current symbolic music generation methods?\",\"answer\":\"BeatEdit targets the lack of native support for selective modification, where common paradigms must regenerate too much or do not explicitly model which positions should change.\"},{\"question\":\"Why is encoding important for explicit editing effectiveness in symbolic music?\",\"answer\":\"BeatEdit argues that conventional event-based encodings lack structural properties needed for explicit editing, while the Beat encoding provides properties that align with edit operations; cross-encoding evaluation shows notable encoding–method interaction effects.\"},{\"question\":\"How does BeatEdit perform different editing tasks across varying edit densities?\",\"answer\":\"BeatEdit uses three complementary mechanisms: per-token sequence tagging for error correction, iterative refinement for accompaniment editing, and tag-then-fill for segment completion, all under a shared encoding and pretrained backbone.\"}]",1784208921,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"beatedit-symbolic-music-generation-as-explicit-editing","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/beatedit-symbolic-music-generation-as-explicit-editing/86147/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What limitation does BeatEdit address in current symbolic music generation methods?","Question",{"text":75,"@type":76},"BeatEdit targets the lack of native support for selective modification, where common paradigms must regenerate too much or do not explicitly model which positions should change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is encoding important for explicit editing effectiveness in symbolic music?",{"text":80,"@type":76},"BeatEdit argues that conventional event-based encodings lack structural properties needed for explicit editing, while the Beat encoding provides properties that align with edit operations; cross-encoding evaluation shows notable encoding–method interaction effects.",{"name":82,"@type":73,"acceptedAnswer":83},"How does BeatEdit perform different editing tasks across varying edit densities?",{"text":84,"@type":76},"BeatEdit uses three complementary mechanisms: per-token sequence tagging for error correction, iterative refinement for accompaniment editing, and tag-then-fill for segment completion, all under a shared encoding and pretrained backbone.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]