Singing Beat Tracking With Self-supervised Front-end and Linear TransformersĪuthors: Mojtaba Heydari, Zhiyao Duan Comments: 8 Pages.
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DLB significantlyimproves beat tracking accuracy over state-of-the-art OBT methods,yielding a similar performance to offline methods. As such, without waiting at the beginning toreceive a chunk, it provides an immediate beat tracking response,which is critical for many OBT applications. Meanwhile,our proposed method only uses activation of the current time frameto infer beat positions.
Most preexisting OBTmethods either apply some offline approaches to a moving windowcontaining past data to make predictions about future beat positionsor must be primed with past data at startup to initialize. DLB feeds theactivations of a unidirectional RNN into an enhanced Monte-Carlolocalization model to infer beat positions. We propose Don’t Look back! (DLB), a novel approachoptimized for efficiency when performing OBT. Dueto the inaccessibility of future data and the need to make inferencein real-time. Online beat tracking (OBT) has always been a challenging task. viXra:2208.0173 submitted on 03:40:39ĭon’t Look Back: an Online Beat Tracking Method Using RNN and Enhanced Particle FilteringĪuthors: Mojtaba Heydari, Zhiyao Duan Comments: 5 Pages. 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. 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.
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. 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.
Furthermore, systems which attempt to estimate rhythmic information in real-time must be causal and must produce estimates quickly and efficiently. Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective. The online estimation of rhythmic information, such as beat positions, downbeat positions, and meter, is critical for many real-time music applications. The 22rd International Society for Music Information Retrieval Conference (ISMIR 2021) viXra:2209.0007 submitted on 01:35:30īeatnet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter TrackingĪuthors: Mojtaba Heydari, Frank Cwitkowitz, Zhiyao Duan Comments: 8 Pages. In this paper, we will propose a method for learning signals related to a data frame $D_$. Predictive Signals Obtained from Bayesian Network and Entropy MinimizationĪuthors: Ait-Taleb Nabil Comments: 15 Pages.
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