{"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/a-lightweight-instrument-agnostic-model-for","title":"A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation","arxiv_id":"2203.09893","date":"2022-03-18","proceeding":null,"authors":["Rachel M. Bittner","Juan José Bosch","David Rubinstein","Gabriel Meseguer-Brocal","Sebastian Ewert"],"abstract":"Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically been reported for systems focusing on specific settings, e.g. instrument-specific systems tend to yield improved results over instrument-agnostic methods. Similarly, higher accuracy can be obtained when only estimating frame-wise $f_0$ values and neglecting the harder note event detection. Despite their high accuracy, such specialized systems often cannot be deployed in the real-world. Storage and network constraints prohibit the use of multiple specialized models, while memory and run-time constraints limit their complexity. In this paper, we propose a lightweight neural network for musical instrument transcription, which supports polyphonic outputs and generalizes to a wide variety of instruments (including vocals). Our model is trained to jointly predict frame-wise onsets, multipitch and note activations, and we experimentally show that this multi-output structure improves the resulting frame-level note accuracy. Despite its simplicity, benchmark results show our system's note estimation to be substantially better than a comparable baseline, and its frame-level accuracy to be only marginally below those of specialized state-of-the-art AMT systems. With this work we hope to encourage the community to further investigate low-resource, instrument-agnostic AMT systems.","url_abs":"https://arxiv.org/abs/2203.09893v2","url_pdf":"https://arxiv.org/pdf/2203.09893v2.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":"a-lightweight-instrument-agnostic-model-for","repo_url":"https://github.com/spotify/basic-pitch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"music-transcription","task_name":"Music Transcription"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/music-transcription-on-slakh2100","task":"Music Transcription","dataset":"Slakh2100","model":"Basic Pitch","rank_in_archive_order":6,"of":6,"metrics":{"note-level F-measure-no-offset (Fno)":"0.43"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2203.09893","atlas_url":"https://app.syntology.ai/?focus=2203.09893","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09893"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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