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.

本文介绍了一种基于储层计算的新方法，用于预测周期性时间序列，特别适用于生成音乐节奏等需要精确预测节奏的任务。我们的方法利用储层计算，并以预测人类节奏感知为最终目标。我们的网络准确地预测人类频率感知范围内的节奏信号。模型结构包含主要和中间神经元，用于捕捉和传递节奏信息。两个参数矩阵 c 和 k 调节储层的动态特性。我们提出了一个损失函数来调整训练后的 c，并引入了一种动态选择机制来调整 k，以便关注具有杰出贡献的区域。在多样化的测试集上的实验结果展示了准确的预测，并通过实时调整储层的 c 和 k 进一步提高了预测性能。比较评估结果显示其相对于传统模型具有更出色的性能。