用音乐转录模型提升钢琴伴奏生成的准确度
AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model
- 将成熟音乐转录模型能力融入钢琴伴奏生成流程
- 在MUSDB18数据集上音符匹配率优于现有方法
- 适合音乐生成与音频处理研究者参考
近年来，深度学习推动了自动钢琴伴奏生成技术的发展。然而，现有模型在表现力和对原曲的忠实度方面仍有提升空间。为此，本文提出AMT-APC学习算法，利用成熟的自动音乐转录模型（Automatic Music Transcription, AMT）能力，增强钢琴伴奏生成的准确性。通过微调方式融合转录模型的音高、时序和力度信息，使生成结果更贴近原始音频。实验表明，在MUSDB18基准测试中，AMT-APC在音符对齐精度和音轨还原质量上均优于现有方法，显著提升了生成伴奏的保真度。
原文摘要 · Abstract (English)
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.
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