Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
This work addresses source separation in applications like communications or audio processing, but it is incremental as it builds on existing data-driven and cyclostationary signal methods.
The paper tackles single-channel source separation for cyclostationary signals by establishing a lower bound on mean squared error and proposing a U-Net deep learning method that approaches optimal performance with reduced computational burden, demonstrating competitive results in simulations.
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.