Results 31 to 40 of about 191,485 (185)
Blind nonnegative source separation using biological neural networks
, 2017 Blind source separation, i.e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing.
Chklovskii, Dmitri B. +2 more
core +1 more source
Wavelet Domain Image Separation
, 2002 In this paper, we consider the problem of blind signal and image separation using a sparse representation of the images in the wavelet domain. We consider the problem in a Bayesian estimation framework using the fact that the distribution of the wavelet ...
Ichir, Mahieddine, Mohammad-Djafari, Ali
core +3 more sources
Algorithm of underdetermined convolutive blind source separation for high reverberation environment
Tongxin xuebao, 2023 To separate the underdetermined convolutive mixture signals in the high reverberation environment, a novel algorithm of underdetermined convolutive blind source separation was proposed.Aiming at the influence of high reverberation environment, a global ...
Yuan XIE +3 more
doaj +2 more sources
Convolutive Blind Source Separation for Communication Signals Based on the Sliding Z-Transform
IEEE Access, 2020 Convolutive blind source separation (CBSS) is one of the main branches in the field of intelligent signal processing. Inspired by the thought of sliding discrete Fourier transform (DFT), an idea of the sliding Z-transform is introduced in the present ...
Yinjie Jia, Pengfei Xu
doaj +1 more source
Underdetermined blind source separation based on Fuzzy C-Means and Semi-Nonnegative Matrix Factorization [PDF]
, 2012 Conventional blind source separation is based on over-determined with more sensors than sources but the underdetermined is a challenging case and more convenient to actual situation.
Alshabrawy, Ossama S. +3 more
core +1 more source
Overcomplete Blind Source Separation by Combining ICA and Binary Time-Frequency Masking [PDF]
, 2005 A limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors.
Kjems, Ulrik +3 more
core +3 more sources
Multiarray Signal Processing: Tensor decomposition meets compressed sensing [PDF]
, 2009 We discuss how recently discovered techniques and tools from compressed sensing can be used in tensor decompositions, with a view towards modeling signals from multiple arrays of multiple sensors.
Comon, Pierre, Lim, Lek-Heng
core +6 more sources
Single-Channel Blind Image Separation Based on Transformer-Guided GAN
Sensors, 2023 Blind source separation (BSS) has been a great challenge in the field of signal processing due to the unknown distribution of the source signal and the mixing matrix.
Yaya Su +3 more
doaj +1 more source
On a Real-Time Blind Signal Separation Noise Reduction System
International Journal of Reconfigurable Computing, 2018 Blind signal separation has been studied extensively in order to tackle the cocktail party problem. It explores spatial diversity of the received mixtures of sources by different sensors. By using the kurtosis measure, it is possible to select the source
Ka Fai Cedric Yiu, Siow Yong Low
doaj +1 more source
EURASIP Journal on Advances in Signal Processing, 2022 The blind signal separation (BSS) algorithm obtains each original/source signal from the observed signal collected by the receiving antenna or sensor. Objective/loss/cost function and optimization method are two key parts of BSS algorithm.
Lihui Pang +4 more
doaj +1 more source