Doubly sparse models for multiple filter estimation in sparse echoic environments
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We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that ar ...
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We consider the problem of estimating multiple filters from convolutive mixtures of several unknown sources. We propose to exploit both the time-frequency (TF) sparsity of the sources and the sparsity of the mixing filters. Our framework consists of: a) a ...
2011
It is well-known that the Gaussian functions and, more generally, their modulations-translations (the Gabor functions) have the unique property of being optimally localized in space and frequency in the sense of Heisenberg's uncertainty principle. In this ...
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A procedure for time-frequency analysis of time series is described, which is mainly inspired by singular-spectrum analysis, but it presents some modifications that allow checking the convergence of the results and extracting the detected spectral componen ...
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Time–frequency analysis of the dynamics of different wake vorticity structures, generated from a triangular prism orientated with its apex edge against the incoming wind, is carried out. Time–frequency analysis of time-series obtained with hot-wire anemome ...
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We propose a framework for blind multiple filter estimation from convolutive mixtures, exploiting the time-domain sparsity of the mixing filters and the disjointness of the sources in the time-frequency domain. The proposed framework includes two steps: (a ...
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