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Bilnd Source Separation in Nonlinear Mixtures
, Ph.D. Dissertation Sharif University of Technology ; Babaiezadeh, Massoud (Supervisor) ; Jutten, Christian (Co-Supervisor) ; Rivet, Bertrand (Co-Supervisor)
Abstract
Blind Source Separation (BSS) is a technique for estimating individual source components from their mixtures at multiple sensors, where the mixing model is unknown. Although it has been mathematically shown that for linear mixtures, under mild conditions, mutually independent sources can be reconstructed up to accepted ambiguities, there is not such theoretical basis for general nonlinear models. This is why there are relatively few resultsin the literature in this regard in the recent decades, which are focused on specific structured nonlinearities.In the present study, the problem is tackled using a novel approach utilizing temporal information of the signals. The original idea followed in...
Two multimodal approaches for single microphone source separation
, Article European Signal Processing Conference, 28 August 2016 through 2 September 2016 ; Volume 2016-November , 2016 , Pages 110-114 ; 22195491 (ISSN ; 9780992862657 (ISBN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2016
Abstract
In this paper, the problem of single microphone source separation via Nonnegative Matrix Factorization (NMF) by exploiting video information is addressed. Respective audio and video modalities coming from a single human speech usually have similar time changes. It means that changes in one of them usually corresponds to changes in the other one. So it is expected that activation coefficient matrices of their NMF decomposition are similar. Based on this similarity, in this paper the activation coefficient matrix of the video modality is used as an initialization for audio source separation via NMF. In addition, the mentioned similarity is used for post-processing and for clustering the rows...
Multimodal soft nonnegative matrix go-factorization for convolutive source separation
, Article IEEE Transactions on Signal Processing ; Volume 65, Issue 12 , 2017 , Pages 3179-3190 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
2017
Abstract
In this paper, the problem of convolutive source separation via multimodal soft Nonnegative Matrix Co-Factorization (NMCF) is addressed. Different aspects of a phenomenon may be recorded by sensors of different types (e.g., audio and video of human speech), and each of these recorded signals is called a modality. Since the underlying phenomenon of the modalities is the same, they have some similarities. Especially, they usually have similar time changes. It means that changes in one of them usually correspond to changes in the other one. So their active or inactive periods are usually similar. Assuming this similarity, it is expected that the activation coefficient matrices of their...
A new algorithm for multimodal soft coupling
, Article 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017, 21 February 2017 through 23 February 2017 ; Volume 10169 LNCS , 2017 , Pages 162-171 ; 03029743 (ISSN); 9783319535463 (ISBN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
Springer Verlag
2017
Abstract
In this paper, the problem of multimodal soft coupling under the Bayesian framework when variance of probabilistic model is unknown is investigated. Similarity of shared factors resulted from Nonnegative Matrix Factorization (NMF) of multimodal data sets is controlled in a soft manner by using a probabilistic model. In previous works, it is supposed that the probabilistic model and its parameters are known. However, this assumption does not always hold. In this paper it is supposed that the probabilistic model is already known but its variance is unknown. So the proposed algorithm estimates the variance of the probabilistic model along with the other parameters during the factorization...
Blind source separation in nonlinear mixtures: separability and a basic algorithm
, Article IEEE Transactions on Signal Processing ; Volume 65, Issue 16 , 2017 , Pages 4339-4352 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
2017
Abstract
In this paper, a novel approach for performing blind source separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS problem to local linear ones varying along the time, using the derivatives of both sources and observations. Taking into account the proposed idea, numerous algorithms can be developed performing the separation. In this regard, an algorithm, supported by simulation results, is also proposed in this paper. It can be seen that the algorithm well...
Fetal electrocardiogram R-peak detection using robust tensor decomposition and extended Kalman filtering
, Article Computing in Cardiology ; Volume 40 , 2013 , Pages 189-192 ; 23258861 (ISSN) ; 9781479908844 (ISBN) ; Niknazar, M ; Jutten, C ; Shamsollahi, M. B ; Rivet, B ; Sharif University of Technology
2013
Abstract
In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG signals. The obtained average scores of event 4 and 5 on...
Relationships between nonlinear and space-variant linear models in hyperspectral image unmixing
, Article IEEE Signal Processing Letters ; Volume 24, Issue 10 , 2017 , Pages 1567-1571 ; 10709908 (ISSN) ; Ehsandoust, B ; Chanussot, J ; Rivet, B ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2017
Abstract
Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e., the intraclass variability of the materials. The former limitation has been addressed by designing nonlinear mixture models, whereas the second can be dealt with by using (usually linear)...
MOA 2010-BLG-477Lb: Constraining the mass of a microlensing planet from microlensing parallax, orbital motion, and detection of blended light
, Article Astrophysical Journal ; Volume 754, Issue 1 , 2012 ; 0004637X (ISSN) ; Shin, I. G ; Han, C ; Fouqué, P ; Gould, A ; Menzies, J. W ; Beaulieu, J. P ; Bennett, D. P ; Bond, I. A ; Dong, S ; Heyrovsk, D ; Marquette, J. B ; Marshall, J ; Skowron, J ; Street, R. A ; Sumi, T ; Udalski, A ; Abe, L ; Agabi, K ; Albrow, M. D ; Allen, W ; Bertin, E ; Bos, M ; Bramich, D. M ; Chavez, J ; Christie, G. W ; Cole, A. A ; Crouzet, N ; Dieters, S ; Dominik, M ; Drummond, J ; Greenhill, J ; Guillot, T ; Henderson, C. B ; Hessman, F. V ; Horne, K ; Hundertmark, M ; Johnson, J. A ; Jorgensen, U. G ; Kandori, R ; Liebig, C ; Mékarnia, D ; McCormick, J ; Moorhouse, D ; Nagayama, T ; Nataf, D ; Natusch, T ; Nishiyama, S ; Rivet, J. P ; Sahu, K. C ; Shvartzvald, Y ; Thornley, G ; Tomczak, A. R ; Tsapras, Y ; Yee, J. C ; Batista, V ; Bennett, C. S ; Brillant, S ; Caldwell, J. A. R ; Cassan, A ; Corrales, E ; Coutures, C ; Dominis Prester, D ; Donatowicz, J ; Kubas, D ; Martin, R ; Williams, A ; Zub, M ; Andrade De Almeida, L ; Depoy, D. L ; Gaudi, B. S ; Hung, L. W ; Jablonski, F ; Kaspi, S ; Klein, N ; Lee, C. U ; Lee, Y ; Koo, J. R ; Maoz, D ; Muñoz, J. A ; Pogge, R. W ; Polishook, D ; Shporer, A ; Abe, F ; Botzler, C. S ; Chote, P ; Freeman, M ; Fukui, A ; Furusawa, K ; Harris, P ; Itow, Y ; Kobara, S ; Ling, C. H ; Masuda, K ; Matsubara, Y ; Miyake, N ; Ohmori, K ; Ohnishi, K ; Rattenbury, N. J ; Saito, T ; Sullivan, D. J ; Suzuki, D ; Sweatman, W. L ; Tristram, P. J ; Wada, K ; Yock, P. C. M ; Szymański, M. K ; Soszyński, I ; Kubiak, M ; Poleski, R ; Ulaczyk, K ; Pietrzyński, G ; Wyrzykowski, Ł ; Kains, N ; Snodgrass, C ; Steele, I. A ; Alsubai, K. A ; Bozza, V ; Browne, P ; Burgdorf, M. J ; Calchi Novati, S ; Dodds, P ; Dreizler, S ; Finet, F ; Gerner, T ; Hardis, S ; Harpsoe, K ; Hinse, T. C ; Kerins, E ; Mancini, L ; Mathiasen, M ; Penny, M. T ; Proft, S ; Rahvar, S ; Ricci, D ; Scarpetta, G ; Schäfer, S ; Schönebeck, F ; Southworth, J ; Surdej, J ; Wambsganss, J ; Sharif University of Technology
IOP
2012
Abstract
Microlensing detections of cool planets are important for the construction of an unbiased sample to estimate the frequency of planets beyond the snow line, which is where giant planets are thought to form according to the core accretion theory of planet formation. In this paper, we report the discovery of a giant planet detected from the analysis of the light curve of a high-magnification microlensing event MOA 2010-BLG-477. The measured planet-star mass ratio is q = (2.181 ± 0.004) × 10-3 and the projected separation is s = 1.1228 ± 0.0006 in units of the Einstein radius. The angular Einstein radius is unusually large θE = 1.38 ± 0.11 mas. Combining this measurement with constraints on the...