AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model

TL;DR

This paper introduces AMT-APC, a learning algorithm that enhances automatic piano cover generation by fine-tuning automatic music transcription models, resulting in more accurate reproductions of original tracks.

Contribution

The paper presents a novel fine-tuning approach for automatic music transcription models to improve automatic piano cover quality.

Findings

AMT-APC outperforms existing models in accuracy

Improved expressiveness and fidelity in generated covers

Demonstrated effectiveness through experimental results

Abstract

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.