研究噪声对钢琴自动转录的影响并优化数据增强策略
Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation
- 在不同信噪比下注入白噪声，测试模型鲁棒性
- 噪声增强训练使模型在低信噪比下性能下降减少40%
- 适合需要抗噪能力的音乐转录系统开发者
近期自动钢琴转录（APT）技术取得显著进展，但噪声环境对系统性能的影响仍缺乏深入研究。本研究考察了不同信噪比（SNR）下白噪声对先进APT模型的影响，并评估了在噪声增强数据上训练的Onsets and Frames模型的表现。实验结果表明，在低信噪比条件下，经过噪声数据增强训练的模型性能下降幅度相比基准模型减少了40%。该研究为构建在多样声学条件下保持稳定表现的转录模型提供了初步依据。
原文摘要 · Abstract (English)
Recent advancements in Automatic Piano Transcription (APT) have significantly improved system performance, but the impact of noisy environments on the system performance remains largely unexplored. This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models and evaluates the performance of the Onsets and Frames model when trained on noise-augmented data. We hope this research provides valuable insights as preliminary work toward developing transcription models that maintain consistent performance across a range of acoustic conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。