IDSD：基于迭代深度学习的信号分解
IDSD: Iterative Deep-Learning-Based Signal Decomposition
AI总结：
针对单变量信号分解的不适定问题，提出IDSD模型，用数据驱动先验替代固定窄带先验，可自适应提取分量，在合成数据及潮汐波、生理测量数据集上性能更优。
AI中文摘要：
数据驱动的信号分解方法可结合信号特性，以灵活自适应的方式将信号分解为其底层分量。本文关注单变量信号，其分解是不适定问题。经典单变量方法（如变分模态分解）通过窄带先验等方式约束解空间，且常需预先知晓分量数量，这些假设限制了算法在某些实际应用中的使用。相反，本文利用神经网络的灵活性，用数据驱动先验替代固定窄带先验。所提模型名为Iterative Deep-Learning-Based Signal Decomposition（IDSD），可迭代地从信号中提取数量自适应、类型多样的分量，对分量带宽无限制。本文在含合成数据的受控设置及两个涉及潮汐波和生理测量的真实数据集上，均验证了IDSD的性能更优。
英文摘要：
Data-driven signal decomposition methods decompose a signal into its underlying components in a flexible and adaptive way, taking into account the signal characteristics. Here we focus on univariate signals, whose decomposition is ill-posed. Classical univariate approaches -- like variational mode decomposition -- constrain the solution space (e.g. through narrowband priors), and often require the number of components to be known in advance. These assumption, however, limit the algorithm's usage in certain real-life applications. Instead, we exploit the flexibility of neural networks to replace fixed (narrowband) priors with data-driven priors. Our model, called Iterative Deep-Learning-Based Signal Decomposition (IDSD), iteratively extracts an adaptive number of various types of components from a signal, with no restrictions on the bandwidth of a component. We show superior performance of IDSD both in a controlled setup with synthetic data, and on two real datasets concerning tidal waves and physiological measurements.
