cs.SDMar 29, 2026
SaveAdvancing Multi-Instrument Music Transcription: Results from the 2025 AMT Challenge
Abstract
This paper presents the results of the 2025 Automatic Music Transcription (AMT) Challenge, an online competition to benchmark progress in multi-instrument transcription. Eight teams submitted valid solutions; two outperformed the baseline MT3 model. The results highlight both advances in transcription accuracy and the remaining difficulties in handling polyphony and timbre variation. We conclude with directions for future challenges: broader genre coverage and stronger emphasis on instrument detection.
Figures & tables
Figure 1: Excerpts of the sample music available to the participants.
Figure 2: End-to-end data flow of the transcription challenge.
Figure 3: Evaluation Method. Top: Reference. Bottom: Estimated notes.
| Rank | Model Name | F1 Score | Precision | Recall | Overlap | Runtime |
|---|---|---|---|---|---|---|
| 1 | MIROS | 0.5998 | 0.6558 | 0.5724 | 0.7391 | 22.05 |
| 2 | YourMT3-YPTF-MoE-M | 0.5938 | 0.6010 | 0.5888 | 0.7305 | 12.60 |
| 3 | YourMT3-YPTF-S | 0.5581 | 0.5565 | 0.5615 | 0.7326 | 15.40 |
| 4 | YourMT3-P | 0.3947 | 0.3966 | 0.3985 | 0.7263 | 14.99 |
| 5 | MT3 [ 9 ] (baseline) | 0.3932 | 0.3811 | 0.4115 | 0.7180 | 20.19 |
| 6 | YourMT3-YPTF-SP-V | 0.3305 | 0.3280 | 0.3358 | 0.7147 | 14.50 |
Table 1: Results from all evaluated systems. Multiple variants and public baselines are included; MT3 serves as the reference baseline. Runtime is measured by ms.
| Model | Instrument Count | F-measure | Precision | Recall |
|---|---|---|---|---|
| MT3 (Baseline) | Count = 1 (n=6) | 0.5483 0.3898 | 0.5548 0.3974 | 0.5422 0.3829 |
| MT3 (Baseline) | Count = 2 (n=24) | 0.3792 0.1829 | 0.3661 0.1808 | 0.3966 0.1894 |
| MT3 (Baseline) | Count = 3 (n=46) | 0.2559 0.1388 | 0.2475 0.1409 | 0.2681 0.1383 |
| MIROS | Count = 1 (n=6) | 0.7193 0.2103 | 0.9067 0.2286 | 0.6233 0.2295 |
| MIROS | Count = 2 (n=24) | 0.5004 0.2403 | 0.5460 0.2296 | 0.4749 0.2530 |
| MIROS | Count = 3 (n=46) | 0.4367 0.2012 | 0.4643 0.2044 | 0.4255 0.2101 |
Table 2: Performance comparison across exact instrument counts (1, 2, and 3 instruments) for the top two models alongside the MT3 baseline. Values are reported as mean standard deviation. Welch’s t-test results are shown above in the text. The variable denotes the number of evaluated pieces belonging to each instrument count category.
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Automatic Music Transcription
Automatic music transcription converts recorded music into symbolic notes, rhythms, instruments, and sometimes readable scores or tablature.
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