Papers › A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and...

A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation

18 Mar 2022arXiv:2203.09893archive 2025-07-28

Rachel M. Bittner, Juan José Bosch, David Rubinstein, Gabriel Meseguer-Brocal, Sebastian Ewert

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 f₀ 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.

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create_lowpass_filter spotify/basic-pitch/basic_pitch/layers/nnaudio.py official repository unverified Apache-2.0 (permissive) · 8d6186c45472ceb6 · report
early_downsample spotify/basic-pitch/basic_pitch/layers/nnaudio.py official repository unverified Apache-2.0 (permissive) · a18369c08b1f3ec8 · report
log_base_b spotify/basic-pitch/basic_pitch/layers/math.py official repository unverified Apache-2.0 (permissive) · 4875d60a49834898 · report
next_power_of_2 spotify/basic-pitch/basic_pitch/layers/nnaudio.py official repository unverified Apache-2.0 (permissive) · 44c2b26e93e92ea6 · report
onset_loss spotify/basic-pitch/basic_pitch/models.py official repository unverified Apache-2.0 (permissive) · 65c254c146d38958 · report
transcription_loss spotify/basic-pitch/basic_pitch/models.py official repository unverified Apache-2.0 (permissive) · fe8a991399b451fc · report
weighted_transcription_loss spotify/basic-pitch/basic_pitch/models.py official repository unverified Apache-2.0 (permissive) · 0f90f8186778e745 · report

Tasks

Music Transcription

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Music Transcription Slakh2100 Basic Pitch note-level F-measure-no-offset (Fno) 0.43 #6 of 6 Archive leaderboard report

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