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However, recent model designs for MSS were mainly motivated by other audio processing tasks or other research fields, while the intrinsic characteristics and patterns of the music signals were not fully discovered. In this paper, we propose band-split RNN (BSRNN), a frequency-domain model that explictly splits the spectrogram of the mixture into subbands and perform interleaved band-level and sequence-level modeling. The choices of the bandwidths of the subbands can be determined by a priori knowledge or expert knowledge on the characteristics of the target source in order to optimize the performance on a certain type of target musical instrument. To better make use of unlabeled data, we also describe a semi-supervised model finetuning pipeline that can further improve the performance of the model. Experiment results show that BSRNN trained only on MUSDB18-HQ dataset significantly outperforms several top-ranking models in Music Demixing (MDX) Challenge 2021, and the semi-supervised finetuning stage further improves the performance on all four instrument tracks.","url_abs":"https://arxiv.org/abs/2209.15174v1","url_pdf":"https://arxiv.org/pdf/2209.15174v1.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":"music-source-separation-with-band-split-rnn","repo_url":"https://github.com/amanteur/BandSplitRNN-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"music-source-separation-with-band-split-rnn","repo_url":"https://github.com/crlandsc/music-demixing-with-band-split-rnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"music-source-separation-with-band-split-rnn","repo_url":"https://github.com/naba89/iseparate-sdx","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"music-source-separation","task_name":"Music Source Separation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/music-source-separation-on-musdb18","task":"Music Source Separation","dataset":"MUSDB18","model":"Band-Split RNN (semi-sup.)","rank_in_archive_order":3,"of":27,"metrics":{"SDR (avg)":"8.97","SDR (bass)":"8.16","SDR (drums)":"10.15","SDR (other)":"7.08","SDR (vocals)":"10.47"},"uses_additional_data":true},{"leaderboard":"/sota/music-source-separation-on-musdb18","task":"Music Source Separation","dataset":"MUSDB18","model":"Band-Split RNN","rank_in_archive_order":5,"of":27,"metrics":{"SDR (avg)":"8.23","SDR (bass)":"7.51","SDR (drums)":"8.58","SDR (other)":"6.62","SDR (vocals)":"10.21"},"uses_additional_data":false},{"leaderboard":"/sota/music-source-separation-on-musdb18-hq","task":"Music Source Separation","dataset":"MUSDB18-HQ","model":"Band-Split RNN (semi-sup.)","rank_in_archive_order":7,"of":14,"metrics":{"SDR (avg)":"8.97","SDR (bass)":"8.16","SDR (drums)":"10.15","SDR (others)":"7.08","SDR (vocals)":"10.47"},"uses_additional_data":true},{"leaderboard":"/sota/music-source-separation-on-musdb18-hq","task":"Music Source Separation","dataset":"MUSDB18-HQ","model":"Band-Split RNN","rank_in_archive_order":9,"of":14,"metrics":{"SDR (avg)":"8.24","SDR (bass)":"7.22","SDR (drums)":"9.01","SDR (others)":"6.70","SDR (vocals)":"10.01"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2209.15174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.15174"}},"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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