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Advancing Multi-Instrument Music Transcription: Results from the 2025 AMT Challenge

Ojas Chaturvedi, Kayshav Bhardwaj, Tanay Gondil, Benjamin Shiue-Hal Chou, Kristen Yeon-Ji Yun, Yung-Hsiang Lu, Yujia Yan, Sungkyun Chang

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.

Advancing 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.

Paper Structure

This paper contains 7 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Previous Transcription Competitions
  3. Evaluation Methodology
  4. Evaluation System
  5. Test Data
  6. Competition Results
  7. Conclusion and Future Directions

Figures (3)

  • 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.