Signal Restoration Based on Temporal Structure and Multi-Layer Architecture

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.

Original languageEnglish
Title of host publication2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538647271
DOIs
Publication statusPublished - 2019 Feb 20
Event2018 IEEE Global Communications Conference, GLOBECOM 2018 - Abu Dhabi, United Arab Emirates
Duration: 2018 Dec 92018 Dec 13

Publication series

Name2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings

Conference

Conference2018 IEEE Global Communications Conference, GLOBECOM 2018
CountryUnited Arab Emirates
CityAbu Dhabi
Period18/12/918/12/13

Fingerprint

Restoration
restoration
Multilayer
Blind source separation
Convex optimization
optimization
Ill-posedness
Blind Source Separation
Diagonalization
Prior Information
Parametric Model
Feature Space
Convex Optimization
Mixture Model
Polynomials
Attenuation
Nonlinear Problem
Degradation
polynomials
estimating

Keywords

  • multi-layer architecture
  • Nonlinear blind source separation
  • temporal structure
  • vanishing component analysis

ASJC Scopus subject areas

  • Information Systems and Management
  • Renewable Energy, Sustainability and the Environment
  • Safety, Risk, Reliability and Quality
  • Signal Processing
  • Modelling and Simulation
  • Instrumentation
  • Computer Networks and Communications

Cite this

Wang, L., & Ohtsuki, T. (2019). Signal Restoration Based on Temporal Structure and Multi-Layer Architecture. In 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings [8647206] (2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GLOCOM.2018.8647206

Signal Restoration Based on Temporal Structure and Multi-Layer Architecture. / Wang, Lu; Ohtsuki, Tomoaki.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. 8647206 (2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, L & Ohtsuki, T 2019, Signal Restoration Based on Temporal Structure and Multi-Layer Architecture. in 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings., 8647206, 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2018 IEEE Global Communications Conference, GLOBECOM 2018, Abu Dhabi, United Arab Emirates, 18/12/9. https://doi.org/10.1109/GLOCOM.2018.8647206
Wang L, Ohtsuki T. Signal Restoration Based on Temporal Structure and Multi-Layer Architecture. In 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. 8647206. (2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings). https://doi.org/10.1109/GLOCOM.2018.8647206
Wang, Lu ; Ohtsuki, Tomoaki. / Signal Restoration Based on Temporal Structure and Multi-Layer Architecture. 2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings).
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