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ICASSP
2011
IEEE
9 years 3 months ago
An adaptive time-frequency resolution approach for Non-negative Matrix Factorization based single channel sound source separatio
In this paper, we propose an adaptive time-frequency resolution approach for the single channel source separation problem. The aim is to improve the quality and intelligibility of...
Serap Kirbiz, Paris Smaragdis
TIFS
2008
133views more  TIFS 2008»
9 years 11 months ago
Kerckhoffs-Based Embedding Security Classes for WOA Data Hiding
Abstract-- It has recently been discovered that using pseudorandom sequences as carriers in spread-spectrum techniques for data-hiding is not at all a sufficient condition for ensu...
François Cayre, Patrick Bas
NIPS
1996
10 years 1 months ago
Maximum Likelihood Blind Source Separation: A Context-Sensitive Generalization of ICA
In the square linear blind source separation problem, one must nd a linear unmixing operator which can detangle the result xi(t) of mixing n unknown independent sources si(t) thro...
Barak A. Pearlmutter, Lucas C. Parra
ICA
2004
Springer
10 years 5 months ago
Blind Source Separation of Linear Mixtures with Singular Matrices
We consider the Blind Source Separation problem of linear mixtures with singular matrices and show that it can be solved if the sources are sufficiently sparse. More generally, we ...
Pando G. Georgiev, Fabian J. Theis
ICA
2007
Springer
10 years 5 months ago
Solving the Permutation Problem in Convolutive Blind Source Separation
Abstract. This paper presents a new algorithm for solving the permutation ambiguity in convolutive blind source separation. When transformed to the frequency domain, the source sep...
Radoslaw Mazur, Alfred Mertins
ICIP
2008
IEEE
10 years 6 months ago
Incorporating known features into a total variation dictionary model for source separation
The goal of this paper is to investigate the impact of dictionary choosing for a total variation dictionary model. After theoretical analysis, we present the experiments in which ...
Tieyong Zeng
ICANN
2009
Springer
10 years 6 months ago
Selective Attention Improves Learning
Abstract. We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented...
Antti Yli-Krekola, Jaakko Särelä, Harri ...
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