Ten lectures on wavelets
Spikes: exploring the neural code
Spikes: exploring the neural code
Array Signal Processing: Concepts and Techniques
Array Signal Processing: Concepts and Techniques
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
MIMO channel blind identification in the presence of spatiallycorrelated noise
IEEE Transactions on Signal Processing
Wavelet-based statistical signal processing using hidden Markovmodels
IEEE Transactions on Signal Processing
Direction-of-arrival estimation of an amplitude-distorted wavefront
IEEE Transactions on Signal Processing
Subspace analysis of spatial time-frequency distribution matrices
IEEE Transactions on Signal Processing
Whitening-rotation-based semi-blind MIMO channel estimation
IEEE Transactions on Signal Processing
Time-invariant orthonormal wavelet representations
IEEE Transactions on Signal Processing
Wavelet footprints: theory, algorithms, and applications
IEEE Transactions on Signal Processing
Maximum likelihood DOA estimation and asymptotic Cramer-Rao boundsfor additive unknown colored noise
IEEE Transactions on Signal Processing
Subspace projection based blind channel order estimation of MIMO systems
IEEE Transactions on Signal Processing
On determination of the number of signals in spatially correlatednoise
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
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We investigate a new approach for the problem of source separation in correlated multichannel signal and noise environments. The framework targets the specific case when nonstationary correlated signal sources contaminated by additive correlated noise impinge on an array of sensors. Existing techniques targeting this problem usually assume signal sources to be independent, and the contaminating noise to be spatially and temporally white, thus enabling orthogonal signal and noise subspaces to be separated using conventional eigendecomposition. In our context, we propose a solution to the problem when the sources are nonorthogonal, and the noise is correlated with an unknown temporal and spatial covariance. The approach is based on projecting the observations onto a nested set of multiresolution spaces prior to eigendecomposition. An inherent invariance property of the signal subspace is observed in a subset of the multiresolution spaces that depends on the degree of approximation expressed by the orthogonal basis. This feature, among others revealed by the algorithm, is eventually used to separate the signal sources in the context of "best basis" selection. The technique shows robustness to source nonstationarities as well as anisotropic properties of the unknown signal propagation medium under no constraints on the array design, and with minimal assumptions about the underlying signal and noise processes. We illustrate the high performance of the technique on simulated and experimental multi-channel neurophysiological data measurements.