Speech acquired from an array of distant microphones is affected by ambient noise and reverberation. Single channel linearly constrained minimum variance (LCMV) filters have been proposed to remove ambient noise. In this paper, an algorithm for joint noise cancellation and dereverberation using a multi channel LCMV filter in the frequency domain is proposed. A single channel LCMV filter which a...

Spatial filtering has a long history of successful application in radar and sonar problems (e.g. [1,2]). The process of spatial filtering is also known as beamforming, since early spatial filters were designed to form pencil beams for either receiving or transmitting signals. More recently, spatial filtering has been applied to EEG and MEG [3–6, 8] to localize intracranial sources of electrical...

In many cases hearing impaired persons suffer from hearing loss in both ears, necessitating two hearing apparatuses. In such cases, the applied speech enhancement algorithms should be capable of preserving the, so called, binaural cues. In this paper, a binaural extension of the linearly constrained minimum variance (LCMV) beamformer is proposed. The proposed algorithm, denoted binaural linearl...

We present a comparison of three methods for the solution of the magnetoencephalography inverse problem. The methods are: a linearly constrained minimum variance beamformer, an algorithm implementing multiple signal classification with recursively applied projection and a particle filter for Bayesian tracking. Synthetic data with neurophysiological significance are analyzed by the three methods...

This paper is concerned with the problem of state estimation for discrete-time linear systems in presence additional (equality or inequality) constraints on (or estimate). By use minimum variance duality, converted into an optimal control problem. Two algorithmic solutions are described: full information estimator (FIE) and moving horizon (MHE). The main result to show that proposed stable sens...

We present a tumor localization method for diffuse optical tomography using linearly constrained minimum variance (LCMV) beamforming. Beamforming is a spatial filtering technique where signals from certain directions can be enhanced while noise and interference from other directions are suppressed. In our method, we tessellate the domain into small voxels and regard each voxel as a possible pos...

In brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) the number of selectable targets is rather limited when each target has its own stimulation frequency. One way to remedy this is by combining frequency- with phase encoding. We introduce a new multivariate spatiotemporal filter, based on Linearly Constrained Minimum Variance (LCMV) beamforming, for discr...

In this paper, we present a linearly constrained minimum variance (LCMV) beamforming approach to real time processing algorithms for target detection and classification in hyperspectral imagery. The only required knowledge for these LCMV-based algorithms is targets of interest. The idea is to design a finite impulse response (FIR) filter to pass through these targets using a set of linear const...

This paper addresses the problem of noise reduction in the time domain where the clean speech sample at every time instant is estimated by filtering a vector of the noisy speech signal. Such a clean speech estimate consists of both the filtered speech and residual noise (filtered noise) as the noisy vector is the sum of the clean speech and noise vectors. Traditionally, the filtered speech is t...