用神经网络提升钢琴音频与MIDI的对齐精度，优于传统方法。
Fine-Tuning MIDI-to-Audio Alignment using a Neural Network on Piano Roll and CQT Representations
- 结合钢琴谱和频谱输入，用CRNN模型学习时间对齐关系。
- 在多个容差窗口下，对齐准确率比DTW高20%。
- 适合音乐信息检索、自动伴奏生成等场景使用。
本文提出一种基于卷积循环神经网络（CRNN）的方法，用于将真人演奏的钢琴音频与松散对齐的MIDI文件进行同步。模型以未对齐的钢琴谱和频谱作为输入，估计出精确对齐的钢琴谱。为训练模型，构建了包含人为演奏时序误差的钢琴曲数据集。实验表明，该模型在不同容差窗口下的对齐准确率比行业标准动态时间规整（DTW）方法最高提升20%。进一步将DTW与CRNN结合，可获得更稳健、一致的对齐效果。结果证明神经网络在推进MIDI-to-audio对齐技术方面具有显著潜力。
原文摘要 · Abstract (English)
In this paper, we present a neural network approach for synchronizing audio recordings of human piano performances with their corresponding loosely aligned MIDI files. The task is addressed using a Convolutional Recurrent Neural Network (CRNN) architecture, which effectively captures spectral and temporal features by processing an unaligned piano roll and a spectrogram as inputs to estimate the aligned piano roll. To train the network, we create a dataset of piano pieces with augmented MIDI files that simulate common human timing errors. The proposed model achieves up to 20% higher alignment accuracy than the industry-standard Dynamic Time Warping (DTW) method across various tolerance windows. Furthermore, integrating DTW with the CRNN yields additional improvements, offering enhanced robustness and consistency. These findings demonstrate the potential of neural networks in advancing state-of-the-art MIDI-to-audio alignment.
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