针对独奏乐器的节奏估计，新模型提升吉他节奏识别准确率38.6%。
Music Tempo Estimation on Solo Instrumental Performance
- 用独奏音乐数据训练时序卷积网络（TCN），提升节奏估计效果。
- 吉他节奏估计的Acc1达99.7%，比预训练模型提升38.6%。
- 结合后处理方法可改善模型在特定乐器上的表现，适合音乐转录应用。
近年来，自动音乐转录已能将音乐音频转化为精准的MIDI。然而，生成的MIDI缺少速度等乐谱标记，阻碍其转换为五线谱。本文研究了当前先进的节奏估计技术，并评估其在独奏乐器音乐上的表现。包括在大量混合人声与乐器音乐上预训练的时序卷积网络（TCN）和循环神经网络（RNN）模型，以及专为独奏乐器表演训练的TCN模型。在鼓、吉他和古典钢琴数据集上的评估显示，采用新训练方案的TCN模型表现最佳。新训练的TCN模型使吉他节奏估计的Acc1提升38.6%，达到99.7%，而预训练模型为61.1%。尽管新模型在古典钢琴节奏估计上准确率是预训练模型的两倍，但其Acc1仍仅为50.9%。为提升深度学习模型性能，我们还研究了其与多种后处理方法的结合。这些后处理技术在模型难以估计特定乐器节奏时显著提升了表现。
原文摘要 · Abstract (English)
Recently, automatic music transcription has made it possible to convert musical audio into accurate MIDI. However, the resulting MIDI lacks music notations such as tempo, which hinders its conversion into sheet music. In this paper, we investigate state-of-the-art tempo estimation techniques and evaluate their performance on solo instrumental music. These include temporal convolutional network (TCN) and recurrent neural network (RNN) models that are pretrained on massive of mixed vocals and instrumental music, as well as TCN models trained specifically with solo instrumental performances. Through evaluations on drum, guitar, and classical piano datasets, our TCN models with the new training scheme achieved the best performance. Our newly trained TCN model increases the Acc1 metric by 38.6% for guitar tempo estimation, compared to the pretrained TCN model with an Acc1 of 61.1%. Although our trained TCN model is twice as accurate as the pretrained TCN model in estimating classical piano tempo, its Acc1 is only 50.9%. To improve the performance of deep learning models, we investigate their combinations with various post-processing methods. These post-processing techniques effectively enhance the performance of deep learning models when they struggle to estimate the tempo of specific instruments.
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