2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLSP-6.2
Paper Title Deep Unfolding Network for Block-Sparse Signal Recovery
Authors Rong Fu, Tsinghua University, China; Vincent Monardo, Carnegie Mellon University, United States; Tianyao Huang, Yimin Liu, Tsinghua University, China
SessionMLSP-6: Compressed Sensing and Learning
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Machine Learning for Signal Processing: [SMDSP-SAP] Sparsity-aware processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Block-sparse signal recovery has drawn increasing attention in many areas of signal processing, where the goal is to recover a high-dimensional signal whose non-zero coefficients only arise in a few blocks from compressive measurements. However, most off-the-shelf data-driven reconstruction networks do not exploit the block-sparse structure. Thus, they suffer from deteriorating performance in block-sparse signal recovery. In this paper, we put forward a block-sparse reconstruction network named Ada-BlockLISTA based on the concept of deep unfolding. Our proposed network consists of a gradient descent step on every single block followed by a block-wise shrinkage step. % with a trainable complex-valued shrinkage function. We evaluate the performance of the proposed Ada-BlockLISTA network through simulations based on the signal model of 2D harmonic retrieval problems.