AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model
This work addresses the need for better automatic piano cover generation for music applications, but it appears incremental as it builds on existing transcription models.
The paper tackles the problem of generating expressive and accurate piano covers by proposing AMT-APC, a learning algorithm that fine-tunes an automatic music transcription model, resulting in improved fidelity to original tracks compared to existing models.
There have been several studies on automatically generating piano covers, and recent advancements in deep learning have enabled the creation of more sophisticated covers. However, existing automatic piano cover models still have room for improvement in terms of expressiveness and fidelity to the original. To address these issues, we propose a learning algorithm called AMT-APC, which leverages the capabilities of automatic music transcription models. By utilizing the strengths of well-established automatic music transcription models, we aim to improve the accuracy of piano cover generation. Our experiments demonstrate that the AMT-APC model reproduces original tracks more accurately than any existing models.