Phase Retrieval Using Deep Dual Alternating Direction Method of Multipliers Network With Deep Sparse Prior Knowledge

作     者：Moogyeong Kim Wonzoo Chung 

作者机构：Department of Artificial Intelligence Korea University Seoul South Korea 

出 版 物：《IEEE Transactions on Audio, Speech and Language Processing》 

年 卷 期：2025年第33卷

页      面：557-569页

基　　金：Artificial Intelligence Graduate School Program Korea government MSIT Institute of Information & communications Technology Planning & Evaluation Artificial Intelligence Innovation Hub Program funded by the Korea government MSIT IITP 

主　　题：Optimization Convex functions Time-domain analysis Spectrogram Signal processing algorithms Vectors Sparse approximation Approximation algorithms Knowledge engineering Time-frequency analysis 

摘      要：In this paper, a deep phase retrieval algorithm for speech signals based on the dual Alternating Direction Method of Multipliers (ADMM) incorporating a deep prior network that exploits the sparsity of speech signals is presented. The proposed network, named DADMM-net, unfolds the dual ADMM for the $\ell _{1}$ -regularized non-convex optimization problem of phase retrieval with several two-dimensional convolutional neural networks (2D-CNNs). In order to efficiently optimize the deep unfolding network for high-dimensional parameter vectors, a novel updating scheme referred to as soft coordinate descent (soft-CD) is proposed, where dual parameter updates are determined through interpolation between the current values and the updated values coordinate-wise with respect to the weights computed by deep networks in each layer. Numerical simulations on a publicly available dataset confirm the state-of-the-art performance of the proposed method in terms of perceptual evaluation of speech quality and short-time objective intelligibility with a significantly faster convergence speed compared to existing methods.