Blind Separation for Wireless Communication Convolutive Mixtures Based on Denoising IVA

Li, Mingchun; Chang, Zhiwei; Zhang, Linghao; Xu, Houdong; Luo, Zhi-Quan; Guo, Ruiming

doi:10.1109/access.2022.3218633

Cited by 1 publication

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The 1D vector generated in this way can be recovered by implementing current sparse recovery tools. Therefore, the essence of the proposed ASTFS-UBSS algorithm lies in its reformulation of the UBSS problem as a sparse signal recovery problem involving estimating the STFT value matrix S s (t, f ) in (2).…”

Section: Source Separation With Sparse Recovery Modelmentioning

confidence: 99%

“…In order to further deal with the signal of interest given the received mixtures, blind source separation (BSS) techniques have exhibited potential ability to extract informative signals and suppress undesirable signals with the aim of improving spectrum efficiency. Stemming from the cocktail party problem, BSS attempts to reconstruct the original signals from observed mixtures without the prior information of mixing weights and original sources, and is widely applied in wireless communication [2,3], speech processing [4,5], image processing [6], biomedicine [7,8], and more.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation

Chen,

Zhang,

Sun

2024

Electronics

View full text Add to dashboard Cite

This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e., M=2, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct N>2 sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array.

show abstract

Section: Source Separation With Sparse Recovery Modelmentioning

confidence: 99%