论文标题
不要回头：使用RNN和增强粒子过滤的在线节拍跟踪方法
Don't look back: an online beat tracking method using RNN and enhanced particle filtering
论文作者
论文摘要
在线节拍跟踪（ABT）一直是一项具有挑战性的任务。由于未来数据的无法获取以及实时推断的必要性。我们建议不要回头！ （DLB），一种在执行ott时优化效率的新方法。 DLB将单向RNN的激活馈送到增强的蒙特卡洛定位模型中，以推断击败位置。大多数先前存在的ABT方法要么将某些离线方法应用于包含过去数据的移动窗口，以便对未来的Beat位置进行预测，或者必须在启动时使用过去的数据进行启动以初始化。同时，我们提出的方法仅利用当前时间框架的激活来推断击败位置。因此，在一开始就没有等待收到块的情况下，它提供了即时的节拍跟踪响应，这对于许多获得应用程序至关重要。 DLB显着提高了与最先进的OBT方法相比的跟踪准确性，从而产生了与离线方法相似的性能。
Online beat tracking (OBT) has always been a challenging task. Due to the inaccessibility of future data and the need to make inference in real-time. We propose Do not Look back! (DLB), a novel approach optimized for efficiency when performing OBT. DLB feeds the activations of a unidirectional RNN into an enhanced Monte-Carlo localization model to infer beat positions. Most preexisting OBT methods either apply some offline approaches to a moving window containing past data to make predictions about future beat positions or must be primed with past data at startup to initialize. Meanwhile, our proposed method only uses activation of the current time frame to infer beat positions. As such, without waiting at the beginning to receive a chunk, it provides an immediate beat tracking response, which is critical for many OBT applications. DLB significantly improves beat tracking accuracy over state-of-the-art OBT methods, yielding a similar performance to offline methods.
