Codes
Updated: May 28, 2012
Latest
updates can be found at the new page:
https://sites.google.com/site/researchbyzhang/software
(1) BSBL Family [Sparse
Bayesian learning for Block Sparse Model](Version: 1.3.3, Updated:
July 30, 2012)
The package includes SBL algorithms for block sparse model when the block partition is known or unknown. Also there is a demo showing how to recover non-sparse signals mimicking telemonitoring scenarios.
(2) T-SBL/T-MSBL [Sparse
Bayesian learning exploiting temporal correlation](Version:
2.3, Updated: July 30,2011)
I strongly suggest you to spend
three minutes in reading the cookbook to use
TSBL (pdf), or the webpage
The codes realize the algorithms in the reference:
[1] Zhilin
Zhang, Bhaskar D. Rao, Sparse Signal
Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian
Learning, IEEE Journal of Selected Topics
in Signal Processing, Special Issue on Adaptive Sparse Representation of Data
and Applications in Signal and Image Processing, 2011
(3) tMFOCUSS
[temporal M-FOCUSS, which exploits temporal correlation]
The codes realize the algorithm in the reference:
[1]
Zhilin Zhang, Bhaskar D. Rao, Iterative
Reweighted Algorithms for Sparse Signal Recovery with Temporally Correlated
Source Vectors, ICASSP 2011
(4) ARSBL [AR
model based sparse Bayesian learning]
The codes realize the algorithm in the reference:
[1] Zhilin
Zhang, Bhaskar D. Rao, Sparse Signal Recovery
in the Presence of Correlated Multiple Measurement Vectors, Proc. of the
35th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2010), Texas, USA, 2010
(5) SBL (Sparse Bayesian Learning)
[For single measurement vector model and multiple measurement
vector model.]
Note: Different to the code by David Wipf, this version is more suitable for algorithm
comparison since some pre-defined parameters are tuned for optimal recovery
quality and a reasonable trade-off between recovery error and speed.
The codes realize the algorithms in the reference:
[1] D.P. Wipf and B.D. Rao, An Empirical
Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem,
IEEE Transactions on Signal Processing, vol. 55, no. 7, July 2007
[2] D.P. Wipf and B.D. Rao, Sparse
Bayesian Learning for Basis Selection, IEEE Transactions on Signal Processing,
vol. 52, No. 8, August 2004
The codes realize the algorithm in the reference:
[1] S.F. Cotter, B.D. Rao,
E. Kjersti, K. Kreutz-Delgado,
Sparse solutions to linear inverse problems with multiple measurement vectors,
IEEE Trans. on Signal Processing, 2005
(7) ICAtoolbox (Current Version: 3.8)
MATLAB based toolbox for blind signal separation (BSS) and
independent component analysis (ICA). The codes were written in a way to better
understand algorithms, so they were not optimized for speed (However, some of
them have been further improved by the Laboratory for Advanced Brain Signal
Processing, and now are available in the famous ICA simulation software: ICALAB for Signal Processing.).
(8) eigBSE
code for blind extraction of sources with temporal correlation
The codes realize the algorithm in the reference:
[1] Zhi-Lin
Zhang, Zhang Yi,
Robust Extraction of
Specific Signals with Temporal Structure, Neurocomputing 69 (7-9) (2006) 888-893
(9) cICA code for the constrained ICA algorithm
Reference:
[1] W. Lu, J.C. Rajapakse, Constrained independent component analysis, in:
Advances in Neural Information Processing Systems, vol. 13 (NIPS 2000), MIT
Press, Cambridge, MA, 2000, pp. 570-576.
[2] Zhi-Lin
Zhang, Morphologically
Constrained ICA for Extracting Weak Temporally Correlated Signals, Neurocomputing 71(7-9) (2008) 1669-1679
(10) TCExt code for blind
extraction of sources based on autocorrelation and adaptive nonlinearities
The codes realize the algorithm in the reference:
[1]
Zhi-Lin Zhang, Liqing
Zhang, A Two-stage
Based Approach for Extracting Periodic Signals, Proc. of the 6th
International Conference on Independent Component Analysis and Blind Signal
Separation (ICA 2006), USA, 2006