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A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing, 1997Adel Belouchrani
exaly +2 more sources
Multi-Task Learning for Blind Source Separation [PDF]
IEEE Transactions on Image Processing, 2018 Blind source separation (BSS) aims to discover the underlying source signals from a set of linear mixture signals without any prior information of the mixing system, which is a fundamental problem in signal and image processing field.
Bo Du +5 more
semanticscholar +5 more sources
Mainlobe Jamming Suppression With Space–Time Multichannel via Blind Source Separation
IEEE Sensors Journal, 2023[[gabstract]][] To address the problem that array radar is seriously affected by complex mainlobe jamming, a method of mainlobe jamming suppression with space–time multichannel via blind source separation (BSS) is proposed.
Zhenshuo Lei +5 more
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2022
Due to the minimum antenna area constrain of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical
Sheng Chang +5 more
semanticscholar +1 more source
Due to the minimum antenna area constrain of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical
Sheng Chang +5 more
semanticscholar +1 more source
The Journal of the Acoustical Society of America, 1999
In signal processing the received data can be modeled as a superposition of a finite number of elementary source signals and an additive noise. Generally, in a multi-sensor environment application, such as underwater acoustics, the principal objective is estimating the number and directions of radiating sources. In the last decade, eigenstructure-based
Miloud Frikel +2 more
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In signal processing the received data can be modeled as a superposition of a finite number of elementary source signals and an additive noise. Generally, in a multi-sensor environment application, such as underwater acoustics, the principal objective is estimating the number and directions of radiating sources. In the last decade, eigenstructure-based
Miloud Frikel +2 more
openaire +2 more sources
Low Latency Online Blind Source Separation Based on Joint Optimization with Blind Dereverberation
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2021This paper presents a new low-latency online blind source separation (BSS) algorithm. Although algorithmic delay of a frequency domain online BSS can be reduced simply by shortening the short-time Fourier transform (STFT) frame length, it degrades the ...
Tetsuya Ueda +5 more
semanticscholar +1 more source
Fast and Stable Blind Source Separation with Rank-1 Updates
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020We propose a new algorithm for the blind source separation of acoustic sources. This algorithm is an alternative to the popular auxiliary function based independent vector analysis using iterative projection (AuxIVA-IP).
Robin Scheibler, Nobutaka Ono
semanticscholar +1 more source
Comput. Biol. Medicine, 2019
Remote photoplethysmography (rPPG), a non-contact technique to estimate heart rates (HR) from video recordings, has attracted much attention from researchers in recent years. It is well-known that rPPG signals can be extracted from low-resolution videos.
Yu Liu +6 more
semanticscholar +1 more source
Remote photoplethysmography (rPPG), a non-contact technique to estimate heart rates (HR) from video recordings, has attracted much attention from researchers in recent years. It is well-known that rPPG signals can be extracted from low-resolution videos.
Yu Liu +6 more
semanticscholar +1 more source
Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid
IEEE Transactions on Neural Networks and Learning Systems, 2020Nonnegative blind source separation (nBSS) is often a challenging inverse problem, namely, when the mixing system is ill-conditioned. In this work, we focus on an important nBSS instance, known as hyperspectral unmixing (HU) in remote sensing.
Chia-Hsiang Lin, J. Bioucas-Dias
semanticscholar +1 more source
Proceedings of the 9th International Conference on Computer and Automation Engineering, 2017
Convolutive blind source separation (BSS) refers the scenario that sources are recorded by multiple sensors in a reverberant environment, which can be depicted as a convolutive mixing model. A big task in convolutive BSS is to identify the number of the source before the sources are separated from mixtures. In this paper, it shows that this problem can
Junjie Yang +3 more
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Convolutive blind source separation (BSS) refers the scenario that sources are recorded by multiple sensors in a reverberant environment, which can be depicted as a convolutive mixing model. A big task in convolutive BSS is to identify the number of the source before the sources are separated from mixtures. In this paper, it shows that this problem can
Junjie Yang +3 more
openaire +1 more source