Title

Union Of Low-Rank Subspaces Detector

Abstract

The problem of signal detection using a flexible and general model is considered. Owing to applicability and flexibility of sparse signal representation and approximation, it has attracted a lot of attention in many signal processing areas. In this study, the authors propose a new detection method based on sparse decomposition in a union of subspaces model. Their proposed detector uses a dictionary that can be interpreted as a bank of matched subspaces. This improves the performance of signal detection, as it is a generalisation for detectors. Low-rank assumption for the desired signals implies that the representations of these signals in terms of some proper bases would be sparse. Their proposed detector exploits sparsity in its decision rule. They demonstrate the high efficiency of their method in the cases of voice activity detection in speech processing.

Publication Date

2-1-2016

Publication Title

IET Signal Processing

Volume

10

Issue

1

Number of Pages

55-62

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1049/iet-spr.2015.0009

Socpus ID

84955614500 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84955614500

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