Advancing Multi-Instrument Music Transcription: Results from the 2025 AMT Challenge
Ojas Chaturvedi ⋅ Kayshav Bhardwaj ⋅ Tanay Gondil ⋅ Benjamin Chou ⋅ Yujia Yan ⋅ Kristen Yeon-Ji Yun ⋅ Yung-Hsiang Lu
2025 Poster
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Workshop: Artificial Intelligence for Music: Where Creativity Meets Computation
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Workshop: Artificial Intelligence for Music: Where Creativity Meets Computation
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.
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