Scanning process usually degrades digital documents due to the contents of the backside of the scanned manuscript. This is often because of the show-through effect, i.e. the backsi...
In this work, we tackle the problem of blind extraction of intermittent sources. Our approach is based on the generalized eigenvector decomposition of covariance matrices and exten...
We introduce the terms strong sub- and super-Gaussianity to refer to the previously introduced class of densities log-concave is x2 and log-convex in x2 respectively. We derive rel...
Jason A. Palmer, Kenneth Kreutz-Delgado, Scott Mak...
Abstract. In this paper, we consider the Independent Component Analysis problem when the hidden sources are non-negative (Non-negative ICA). This problem is formulated as a non-lin...
Wendyam Serge Boris Ouedraogo, Antoine Souloumiac,...
Abstract. In brain computer interface based on motor imagery, covariances matrices are widely used through spatial filters computation and other signal processing methods. Covarian...
We propose combining supervised and unsupervised algorithms in order to improve the performance of multiple-input multipleoutputdigitalcommunication systemswhich makeuseofdecision-...
One potential strength of recurrent neural networks (RNNs) is their – theoretical – ability to find a connection between cause and consequence in time series in an constraint-...
Abstract. The recovery of the mixture of an N-dimensional signal generated by N independent processes is a well studied problem (see e.g. [1,10]) and robust algorithms that solve t...
Harold W. Gutch, Takanori Maehara, Fabian J. Theis
Abstract. In this paper, we propose a method for blind source separation (BSS) of convolutive audio recordings with short blocks of stationary sources, i.e. dynamically changing so...
SMALLbox is a new foundational framework for processing signals, using adaptive sparse structured representations. The main aim of SMALLbox is to become a test ground for explorati...
Ivan Damnjanovic, Matthew E. P. Davies, Mark D. Pl...