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
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Paper Detail

Paper IDSPTM-17.1
Paper Title CONSTRUCTION OF UNIT-NORM TIGHT FRAME BASED PRECONDITIONER FOR SPARSE CODING
Authors Huang Bai, Hangzhou Normal University, China; Chuanrong Hong, Alibaba Group, China; Xiumei Li, Hangzhou Normal University, China
SessionSPTM-17: Sampling, Multirate Signal Processing and Digital Signal Processing 3
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Signal Processing Theory and Methods: [SMDSP-SAP] Sparsity-aware Processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Signal sparse representation (SR) is an evolving research topic. The sparse coding performance is highly dependent on the properties of the system matrix that are usually hard to be guaranteed. Preconditioning is a technique that transforms a linear system into another one with more favorable properties for sparse solution. In this paper, we investigate the problem of constructing suitable preconditioner to improve the performance of the SR system. We formulate the model for designing the preconditioner by making the product of preconditioner and dictionary strictly approximate to a unit-norm tight frame (UNTF) which has been proved valid in facilitating sparse coding. A parametrization and gradient based approach is presented to solve for the UNTF-based preconditioner. Experiments on speech signals are carried out to demonstrate the performance of the proposed method.