BeatNet: CRNN and Particle Filtering for Online Joint Beat, Downbeat and Meter Tracking
BeatNet: CRNN and Particle Filtering for Online Joint Beat, Downbeat and Meter Tracking
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BeatNet：用于在线联合节拍、强拍和节拍跟踪的 CRNN 和粒子过滤
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Z. Duan
中科院分区：
文献类型：
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作者：
M. Heydari;Frank Cwitkowitz;Z. Duan
节奏信息的在线估计，如节拍位置、弱节拍位置和节拍，对于许多实时音乐应用来说是至关重要的。音乐节奏包括时间上复杂的等级关系，使得其分析具有内在的挑战性，有时还具有主观性。此外，试图实时估计节律信息的系统必须是因果的，并且必须快速有效地产生估计。在这项工作中，我们介绍了一个在线的联合拍子，弱拍子和节拍跟踪系统，它利用了因果卷积和递归层，然后在推理过程中使用了一对顺序的蒙特卡罗粒子滤波器。所提出的系统不需要用时间签名来执行弱拍跟踪，而是能够随着时间的推移来估计仪表和调整预测。此外，我们还提出了一种信息门策略，大大降低了推理过程中粒子滤波的计算代价，使系统比以往基于采样的方法快得多。在训练过程中看不到的GTZAN数据集上的实验表明，该系统的性能优于各种在线节拍和弱拍跟踪系统，并获得了与基线离线联合方法相当的性能。
The online estimation of rhythmic information, such as beat positions, downbeat positions, and meter, is critical for many real-time music applications. Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective. Furthermore, systems which attempt to estimate rhythmic information in real-time must be causal and must produce estimates quickly and efficiently. In this work, we introduce an online system for joint beat, downbeat, and meter tracking, which utilizes causal convolutional and recurrent layers, followed by a pair of sequential Monte Carlo particle filters applied during inference. The proposed system does not need to be primed with a time signature in order to perform downbeat tracking, and is instead able to estimate meter and adjust the predictions over time. Additionally, we propose an information gate strategy to significantly decrease the computational cost of particle filtering during the inference step, making the system much faster than previous sampling-based methods. Experiments on the GTZAN dataset, which is unseen during training, show that the system outperforms various online beat and downbeat tracking systems and achieves comparable performance to a baseline offline joint method.