{"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/symphony-generation-with-permutation","title":"Symphony Generation with Permutation Invariant Language Model","arxiv_id":"2205.05448","date":"2022-05-10","proceeding":null,"authors":["Jiafeng Liu","Yuanliang Dong","Zehua Cheng","Xinran Zhang","Xiaobing Li","Feng Yu","Maosong Sun"],"abstract":"In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation. We propose a novel Multi-track Multi-instrument Repeatable (MMR) representation for symphonic music and model the music sequence using a Transformer-based auto-regressive language model with specific 3-D positional embedding. To overcome length overflow when modeling extra-long symphony tokens, we also propose a modified Byte Pair Encoding algorithm (Music BPE) for music tokens and introduce a novel linear transformer decoder architecture as a backbone. Meanwhile, we train the decoder to learn automatic orchestration as a joint task by masking instrument information from the input. We also introduce a large-scale symbolic symphony dataset for the advance of symphony generation research. Empirical results show that the proposed approach can generate coherent, novel, complex and harmonious symphony as a pioneer solution for multi-track multi-instrument symbolic music generation.","url_abs":"https://arxiv.org/abs/2205.05448v2","url_pdf":"https://arxiv.org/pdf/2205.05448v2.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":"symphony-generation-with-permutation","repo_url":"https://github.com/symphonynet/SymphonyNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"audio-generation","task_name":"Audio Generation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"music-generation","task_name":"Music Generation"},{"task_slug":"text-generation","task_name":"Text Generation"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[{"slug":"symphonynet","name":"SymphonyNet","full_name":"SymphonyNet"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/audio-generation-on-symphony-music","task":"Audio Generation","dataset":"Symphony music","model":"SymphonyNet","rank_in_archive_order":1,"of":1,"metrics":{" Human listening average results":"3.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2205.05448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.05448"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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