论文标题
噪音信号中周期性检测的生成模型
Generative Models for Periodicity Detection in Noisy Signals
论文作者
论文摘要
我们引入了一种新的周期性检测算法，用于二元时间序列的事件，即高斯混合物周期性检测算法（GMPDA）。该算法接近周期性检测问题，以推断生成模型的参数。我们指定了两个模型 - 时钟和随机步行 - 描述了两个不同的周期现象，并提供了生成框架。该算法在单个和多个周期性检测以及噪声水平变化的测试用例上取得了良好的结果。还对GMPDA的性能进行了对实际数据，在睡眠期间记录的腿部运动的评估，尽管噪声水平很高，但GMPDA仍能够识别预期的周期。本文的主要贡献是两个新模型，用于生成周期性事件行为和用于多个周期性检测的GMPDA算法，这在噪声下非常准确。
We introduce a new periodicity detection algorithm for binary time series of event onsets, the Gaussian Mixture Periodicity Detection Algorithm (GMPDA). The algorithm approaches the periodicity detection problem to infer the parameters of a generative model. We specified two models - the Clock and Random Walk - which describe two different periodic phenomena and provide a generative framework. The algorithm achieved strong results on test cases for single and multiple periodicity detection and varying noise levels. The performance of GMPDA was also evaluated on real data, recorded leg movements during sleep, where GMPDA was able to identify the expected periodicities despite high noise levels. The paper's key contributions are two new models for generating periodic event behavior and the GMPDA algorithm for multiple periodicity detection, which is highly accurate under noise.
