Results 271 to 280 of about 48,878 (307)

Beyond ICA: robust sparse signal representations

2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512), 2004
In many applications it is necessary to perform some decomposition of observed signals or data in such a way that components have some special properties or structures such as statistical independence, sparsity, smoothness, nonnegativity, prescribed statistical distributions and/or specific temporal structure.
Andrzej Cichocki, Yuanqing Li 0001, Pando G. Georgiev, Shun-ichi Amari   +3 more
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Change detection in streams of signals with sparse representations

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
We propose a novel approach to performing change-detection based on sparse representations and dictionary learning. We operate on observations that are finite support signals, which in stationary conditions lie within a union of low dimensional subspaces.
ALIPPI, CESARE, BORACCHI, GIACOMO, Wohlberg, Brendt   +2 more
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Improved online dictionary learning for sparse signal representation

2014 22nd Signal Processing and Communications Applications Conference (SIU), 2014
In this paper a new dictionary learning algorithm is proposed. Similar to many dictionary learning algorithms, the proposed algorithm alternates between two stages. First, sparse coding stage uses the current dictionary to obtain the sparse representation coefficients. Herein, the orthogonal matching pursuit algorithm is used for sparse coding. Second,
Yeganli, Faezeh, Özkaramanli, Hüseyin
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Sparse Sinusoidal Signal Representation for Speech and Music Signals

2008
We present a sparse representation called Fixed Dimension Modified Sinusoid Model (FD-MSM) for parametric analysis of audible signals including speech, music and mixtures. Compared with other analysis models, the proposed scheme is both pitch independent and appropriate for sparse signal representation commonly found as a favorable choice for speech ...
Pejman Mowlaee, Amirhossein Froghani, Abolghasem Sayadiyan   +2 more
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Decomposition of MEG signals with sparse representations

2007 IEEE 33rd Annual Northeast Bioengineering Conference, 2007
We suggest an iterative method for the decomposition of MEG signals into some user-specified parts. It is based on a technique called morphological component analysis (MCA), which seeks sparse representations. A numerical simulation is carried out to reveal the performance characteristics of this method.
Tolga E. Ozkurt, Mingui Sun, Robert J. Sclabassi   +2 more
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Sparse representation of complex valued signals

2006 International Conference on Computational Intelligence and Security, 2006
Sparse representation of complex valued signals is addressed in this paper. Considering the statistical dependence between real part and imaginary part of a complex valued signal (e.g., the discrete-time Fourier transform of a real valued signal), a special probability density function (PDF) is introduced to describe the complex random variable in this
Zhaoshui He, Shengli Xie, Yuli Fu
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Sparse representation in speech signal processing

SPIE Proceedings, 2003
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals.
Te-Won Lee, Gil-Jin Jang, Oh-Wook Kwon
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Greedy double sparse dictionary learning for sparse representation of speech signals

Speech Communication, 2016
This paper proposes a greedy double sparse (DS) dictionary learning algorithm for speech signals, where the dictionary is the product of a predefined base dictionary, and a sparse matrix. Exploiting the DS structure, we show that the dictionary can be learned efficiently in the coefficient domain rather than the signal domain.
Vinayak Abrol, Pulkit Sharma, Anil Kumar Sao   +2 more
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Clustering on subspaces and sparse representation of signals

48th Midwest Symposium on Circuits and Systems, 2005., 2005
In many practical problems the data X under consideration (given as (m /spl times/ N)-matrix) is of the form X = AS, where the matrices A and S with dimensions m /spl times/ n and n /spl times/ N respectively (often called mixing matrix or dictionary and source matrix) are unknown (m /spl les/ n < N).
P. Georgiev, A. Ralescu
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Implementation of Irregular Meshes for the Sparse Representation of Multidimensional Signals

Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, 2018
The paper is dedicated to development of effective tools of multidimensional digital signal processing on irregular meshes. ANN-based method of irregular mesh generation for intra-frame video coding is developed. The method described is based on artificial neural network implementation.
Sergey Vishnyakov, Yulia Vishnyakova, Elizaveta Sokolova   +2 more
openaire   +1 more source