Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering

Kwangjin Jeong, Masahiro Yukawa, Masao Yamagishi, Isao Yamada

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We propose a novel automatic shrinkage tuning technique for the adaptive proximal forward-backward splitting (APFBS) algorithm. The shrinkage tuning aims to choose an appropriate value of the shrinkage parameter and achieve minimal system mismatch as possible. The system mismatch is approximated based on time-averaged second-order statistics. Numerical examples show that the proposed method achieves performance fairly close to that with a manually chosen shrinkage parameter for colored input signals at some signal to noise ratio (SNR).

本文言語English
ホスト出版物のタイトル2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ4314-4318
ページ数5
ISBN(印刷版)9781538646588
DOI
出版ステータスPublished - 2018 9 10
イベント2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
継続期間: 2018 4 152018 4 20

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2018-April
ISSN(印刷版)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period18/4/1518/4/20

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

フィンガープリント 「Automatic Shrinkage Tuning Robust to Input Correlation for Sparsity-Aware Adaptive Filtering」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル