This paper introduces a novel approach to predicting periodic time series using reservoir computing. The model is tailored to deliver precise forecasts of rhythms, a crucial aspect for tasks such as generating musical rhythm. Leveraging reservoir computing, our proposed method is ultimately oriented towards predicting human perception of rhythm. Our network accurately predicts rhythmic signals within the human frequency perception range. The model architecture incorporates primary and intermediate neurons tasked with capturing and transmitting rhythmic information. Two parameter matrices, denoted as c and k, regulate the reservoir's overall dynamics. We propose a loss function to adapt c post-training and introduce a dynamic selection (DS) mechanism that adjusts $k$ to focus on areas with outstanding contributions. Experimental results on a diverse test set showcase accurate predictions, further improved through real-time tuning of the reservoir via c and k. Comparative assessments highlight its superior performance compared to conventional models.

翻译：本文提出了一种基于储层计算的周期性时间序列预测新方法。该模型专为精确预测节奏而设计，这对生成音乐节奏等任务至关重要。利用储层计算，我们提出的方法最终旨在预测人类对节奏的感知。我们的网络能准确预测人类感知频率范围内的节奏信号。模型架构包含负责捕捉和传递节奏信息的主神经元与中间神经元。两个参数矩阵c和k调控储层的整体动态。我们提出一种损失函数用于训练后自适应调整c，并引入动态选择（DS）机制来调整$k$以聚焦贡献突出的区域。在多样化测试集上的实验结果表明预测精度高，且通过c和k实时调节储层可进一步提升性能。对比评估显示，该模型性能优于传统方法。