@inproceedings{20582da327ae4cd4b17f0b90c1853ccd,
title = "Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation",
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-square-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.",
keywords = "cyclostationary signal processing, deep neural network, Source separation, supervised learning",
author = "Lee, {Gary C.F.} and Amir Weiss and Alejandro Lancho and Jennifer Tang and Yuheng Bu and Yury Polyanskiy and Wornell, {Gregory W.}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 32nd IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2022 ; Conference date: 22-08-2022 Through 25-08-2022",
year = "2022",
doi = "https://doi.org/10.1109/MLSP55214.2022.9943311",
language = "الإنجليزيّة",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
booktitle = "2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022",
address = "الولايات المتّحدة",
}