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