Automatic Music Transcription (AMT) is a crucial technology in music
information processing. Despite recent improvements in performance through
machine learning approaches, existing methods often achieve high accuracy in
domains with abundant annotation data, primarily due to the difficulty of
creating annotation data. A practical transcription model requires an
architecture that does not require an annotation data. In this paper, we
propose an annotation-free transcription model achieved through the utilization
of scalable synthetic audio for pre-training and adversarial domain confusion
using unannotated real audio. Through evaluation experiments, we confirm that
our proposed method can achieve higher accuracy under annotation-free
conditions compared to when learning with mixture of annotated real audio data.
Additionally, through ablation studies, we gain insights into the scalability
of this approach and the challenges that lie ahead in the field of AMT
research.

提出了一种无需注释数据的音乐自动转录模型，通过利用可扩展的合成音频进行预训练和对抗性域混淆，实现了比使用混合注释真实音频数据学习时更高的准确性，在 AMT 研究领域中，揭示了这种方法的可扩展性和面临的挑战。