A wireless network is considered, in which two spatially distributed users transmit narrow-band signals simultaneously over the same channel using the same power. User separation is achieved by oversampling the received signal and formulating a virtual multiple-input multiple-output (MIMO) system based on the resulting polyphase components. Because of oversampling, high correlations can occur b...

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Sparsity of signals in the frequency domain is widely used for blind source separation (BSS) when the number of sources is more than the number of mixtures (underdetermined BSS). In this paper we propose a simple algorithm for detection of points in the time–frequency (TF) plane of the instantaneous mixtures where only single source contributions occur. Samples at these points in the TF plane c...

5 Blind Source Separation (BSS) plays a key role to analyze multichannel data since it aims at recovering unknown underlying elementary sources from observed linear mixtures in an unsupervised way. In a large number of applications, multichannel measurements contain corrupted entries, which are highly detrimental for most BSS techniques. In this article, we introduce a 10 new robust BSS techniq...

The problern of underdetermined blind source sepamtion is addressed. A n adnanced classification method based upon competitive leainin,g is proposed for automatically determining the number of active sources over the obseruatior~. Its ihtroduction in underdetermined blind source separation successfully overcomes th,e drawbock of an existing method, in which the goal of sepamtiny more sources th...

We study the solvability of sparse blind separation of n non-negative sources from m linear mixtures in the under-determined regime m < n. The geometric properties of the mixture matrix and the sparseness structure of the source matrix are closely related to the identification of the mixing matrix. We first illustrate and establish necessary and sufficient conditions for the unique separation f...

We address the blind source separation (BSS) problem for the convolutive mixing case. Second-order statistical methods are employed assuming the source signals are non-stationary and possibly also non-white. The proposed algorithm is based on a joint-diagonalization approach, where we search for a single polynomial matrix that jointly diagonalizes a set of measured spatiotemporal correlation ma...

Non-negative Matrix Factorization (NMF) has already been applied to learn speaker characterizations from single or nonsimultaneous speech for speaker recognition applications. It is also known for its good performance in (blind) source separation for simultaneous speech. This paper explains how NMF can be used to jointly solve the two problems in a multichannel speaker recognizer for simultaneo...

We consider the problem of recovery of a scene recorded through a semirefective medium from its mixture with a virtual reflected image using the blind source separation (BSS) framework. We extend the Sparse ICA (SPICA) approach and apply it to the separation of the desired image from the superimposed images, without having any a priory knowledge about its structure and/or statistics. Advances i...

We present a two stage blind source separation (BSS) algorithm for robot audition. The algorithm is based on a beamforming preprocessing and a BSS algorithm using a sparsity separation criterion. Before the BSS step, we filter the sensors outputs by beamforming filters to reduce the reverberation and the environmental noise. As we are in a robot audition context, the manifold of the sensor arra...