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.
HingeNet: A Harmonic-Aware Fine-Tuning Approach for Beat Tracking
School of Computer Science, Fudan University, Shanghai, China（复旦大学计算机科学学院）；Naval Medical Center, PLA, China（中国人民解放军海军医疗中心）；Graduate School of Informatics, Kyoto University, Kyoto, Japan（京都大学信息科学研究生院）；Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan（广岛大学先进科学与工程研究生院）；Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China（上海智能信息处理重点实验室）
