https://github.com/teradepth/iva
IVA: Independent Vector Analysis implementation
https://github.com/teradepth/iva
blind-source-separation independent-component-analysis independent-vector-analysis unsupervised-learning
Last synced: 2 months ago
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IVA: Independent Vector Analysis implementation
- Host: GitHub
- URL: https://github.com/teradepth/iva
- Owner: teradepth
- Created: 2018-02-11T14:57:52.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-02-11T15:31:08.000Z (over 7 years ago)
- Last Synced: 2025-01-19T17:46:34.103Z (4 months ago)
- Topics: blind-source-separation, independent-component-analysis, independent-vector-analysis, unsupervised-learning
- Language: Matlab
- Homepage:
- Size: 4.88 KB
- Stars: 57
- Watchers: 4
- Forks: 35
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IVA: Independent Vector Analysis
## matlab implementations
ivabss.m
Natural Gradient algorithm for Frecuency Domain Blind source separation based on Independent Vector Analysis
[y, W] = ivabss(x, nfft, maxiter, tol, eta, nsou)
y : separated signals (nsou x N)
W : unmixing matrices (nsou x nmic x nfft/2+1)
x : observation signals (nmic x N),
where nsou is # of sources, nmic is # of mics, and N is # of time frames
nfft : # of fft points (default =1024)
eta : learning rate (default =0.1)
maxiter : # of iterations (default =1000)
tol : When the difference of objective is less than tol,
the algorithm terminates (default =1e-6)
nsou : # of sources (default =nmic)
fiva.m
Fast algorithm for Frecuency Domain Blind source separation
based on Independent Vector Analysis
[y, W] = fivabss(x, nfft, maxiter, tol, nsou)
y : separated signals (nsou x N)
W : unmixing matrices (nsou x nmic x nfft/2+1)
x : observation signals (nmic x N),
where nsou is # of sources, nmic is # of mics, and N is # of time frames
nfft : # of fft points (default =1024)
maxiter : # of iterations (default =1000)
tol : When the increment of likelihood is less than tol,
the algorithm terminates (deault =1e-6)
nsou : # of sources (default =nmic)
## python implementation
TO-DO
## References
[1] Taesu Kim, "Independent Vector Analysis" Ph.D. Dissertation, KAIST, 2007
[2] Taesu Kim, Hagai Attias, Soo-Young Lee, Te-Won Lee, "Blind source separation exploiting higher-order frequency dependencies" IEEE Transactions on Audio, Speech, and Language Processing 15 (1), 2007
[3] Intae Lee, Taesu Kim, Te-Won Lee, "Fast fixed-point independent vector analysis algorithms for convolutive blind source separation" Signal Processing 87 (8), 2007
[4] Taesu Kim, Torbjørn Eltoft, Te-Won Lee, "Independent vector analysis: An extension of ICA to multivariate components" International Conference on Independent Component Analysis and Signal Separation, 2006
[5] Taesu Kim, Intae Lee, Te-Won Lee, "Independent vector analysis: definition and algorithms", Fortieth Asilomar Conference on Signals, Systems and Computers, 2006