NDLI: Orthogonal segmented model for underdetermined blind identification and separation of sources with sparse events

Content Provider	IEEE Xplore Digital Library
Author	Makkiabadi, B. Sanei, S.
Copyright Year	2013
Description	Author affiliation: Fac. of Eng. & Phys. Sci., Univ. of Surrey, Guildford, UK (Makkiabadi, B.; Sanei, S.)
Abstract	In this paper, a novel tensor factorization method based on $k_{a}-SCA$ (called k-SCA in [1]) approach is developed to solve the underdetermined blind source separation (UBSS) and especially underdetermined blind identification (UBI) problems where $k_{a}$ sources are active in each signal segment. Similar to $k_{a}-SCA$ methods we assume our $k_{a}$ is equal to, or less than, the number of sensors minus one when sources are mixed instantaneously. This approach improves the general upper bound for maximum possible number of sources in the second order underdetermind blind identification problem suggested by well known tensor based methods. Alternating constrained optimization approaches are developed to estimate the mixing model and the rank deficient segments. Also this method provides sub-optimum solutions to the UBSS problem. The method is applied to mixtures of synthetic and real signals of sparse events such as instantaneously mixed speech signals. The obtained results show a marked improvement in separability (e.g. it can be used for blind identification and separation of up to 10 speech sources out of 3 sensors) and channel identification compared with other well-established approaches.
Starting Page	1
Ending Page	6
File Size	194884
Page Count	6
File Format	PDF
ISBN	9781479911806
ISSN	15512541
DOI	10.1109/MLSP.2013.6661928
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-22
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Estimation Tensile stress Upper bound Matrix decomposition Covariance matrices Sensors Vectors tensor factorization Underdetermined blind identification blind source separation ka-SCA sparse component analysis
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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