We study the problem of single-channel source separation (SCSS), and focus on
cyclostationary signals, which are particularly suitable in a variety of
application domains. Unlike classical SCSS approaches, we consider a setting
where only examples of the sources are available rather than their models,
inspiring a data-driven approach. For source models with underlying
cyclostationary Gaussian constituents, we establish a lower bound on the
attainable mean squared error (MSE) for any separation method, model-based or
data-driven. Our analysis further reveals the operation for optimal separation
and the associated implementation challenges. As a computationally attractive
alternative, we propose a deep learning approach using a U-Net architecture,
which is competitive with the minimum MSE estimator. We demonstrate in
simulation that, with suitable domain-informed architectural choices, our U-Net
method can approach the optimal performance with substantially reduced
computational burden.

本文研究单通道源信号分离问题，着重于周期稳态信号，提出一种基于 U-Net 架构的深度学习方法，该方法可以接近最优性能，且具有较小的计算负担。

利用周期稳态信号的时间结构进行基于数据驱动的单通道源分离

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

We propose a new framework for single-channel source separation that lies
between the fully supervised and unsupervised setting. Instead of supervision,
we provide input features for each source signal and use convex methods to
estimate the correlations between these features and the unobserved signal
decomposition. We analyze the case of $\ell_2$ loss theoretically and show that
recovery of the signal components depends only on cross-correlation between
features for different signals, not on correlations between features for the
same signal. Contextually supervised source separation is a natural fit for
domains with large amounts of data but no explicit supervision; our motivating
application is energy disaggregation of hourly smart meter data (the separation
of whole-home power signals into different energy uses). Here we apply
contextual supervision to disaggregate the energy usage of thousands homes over
four years, a significantly larger scale than previously published efforts, and
demonstrate on synthetic data that our method outperforms the unsupervised
approach.

提出了一种新的单通道源分离框架，采用凸优化方法以无监督的方式来估计不同信号之间的互相关性，应用于智能电表数据的能源分离，达到了比无监督方法更优的分离效果。