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Automatic Music Transcription (AMT) is a vital technology in the field ofmusic information processing. Despite recent enhancements in performance due tomachine learning techniques, current methods typically attain high accuracy indomains where abundant annotated data is available. Addressing domains with lowor no resources continues to be an unresolved challenge. To tackle this issue,we propose a transcription model that does not require any MIDI-audio paireddata through the utilization of scalable synthetic audio for pre-training andadversarial domain confusion using unannotated real audio. In experiments, weevaluate methods under the real-world application scenario where trainingdatasets do not include the MIDI annotation of audio in the target data domain.Our proposed method achieved competitive performance relative to establishedbaseline methods, despite not utilizing any real datasets of paired MIDI-audio.Additionally, ablation studies have provided insights into the scalability ofthis approach and the forthcoming challenges in the field of AMT research.

Annotation-free Automatic Music Transcription with Scalable Synthetic Data and Adversarial Domain Confusion