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

• URL: http://arxiv.org/abs/2208.10325v1
• Date: Mon, 22 Aug 2022 14:04:56 GMT
• Title: Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation
• Authors: Gary C.F. Lee, Amir Weiss, Alejandro Lancho, Jennifer Tang, Yuheng Bu, Yury Polyanskiy, Gregory W. Wornell
• Abstract summary: We study the problem of single-channel source separation (SCSS) We focus on cyclostationary signals, which are particularly suitable in a variety of application domains. We propose a deep learning approach using a U-Net architecture, which is competitive with the minimum MSE estimator.
• Score: 98.95383921866096
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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