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

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.

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

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.