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» On sparse signal representations
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TSP
2010
14 years 6 months ago
Methods for sparse signal recovery using Kalman filtering with embedded pseudo-measurement norms and quasi-norms
We present two simple methods for recovering sparse signals from a series of noisy observations. The theory of compressed sensing (CS) requires solving a convex constrained minimiz...
Avishy Carmi, Pini Gurfil, Dimitri Kanevsky
ICASSP
2010
IEEE
14 years 9 months ago
A sparse component model of source signals and its application to blind source separation
In this paper, we propose a new method of blind source separation (BSS) for music signals. Our method has the following characteristics: 1) the method is a combination of the spar...
Yu Kitano, Hirokazu Kameoka, Yosuke Izumi, Nobutak...
CISS
2008
IEEE
15 years 6 months ago
Reconstruction of compressively sensed images via neurally plausible local competitive algorithms
Abstract—We develop neurally plausible local competitive algorithms (LCAs) for reconstructing compressively sensed images. Reconstruction requires solving a sparse approximation ...
Robert L. Ortman, Christopher J. Rozell, Don H. Jo...
ICASSP
2011
IEEE
14 years 3 months ago
Robust underdetermined blind audio source separation of sparse signals in the time-frequency domain
We address the problem of blind source separation in the underdetermined and instantaneous mixture case. The proposed method is based on an algorithm developed by Aissa-El-Bey and...
Si-Mohamed Aziz Sbai, Abdeldjalil Aïssa-El-Be...
CISS
2010
IEEE
14 years 3 months ago
Turbo reconstruction of structured sparse signals
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
Philip Schniter