caa a22 4500 606205209 CHVBK 20210128101006.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00034-015-9969-8 doi (NATIONALLICENCE)springer-10.1007/s00034-015-9969-8 Nonnegative Mixture for Underdetermined Blind Source Separation Based on a Tensor Algorithm [Elektronische Daten] [Sunan Ge, Jie Han, Min Han] In this study, a tensor algorithm is proposed to blindly separate an instantaneous linear underdetermined mixture with non-stationary sources and nonnegative mixing matrix. It proceeds in two steps: 1) estimating the mixing matrix and 2) recovering the source signals. First, a canonical tensor model is constructed using a fourth-order cumulant tensor of the observed signals to estimate the mixing matrix. Then, an improved hierarchical alternating least squares algorithm is used to decompose the canonical tensor model, which ensures that all elements of the mixing matrix are positive. Finally, the sources are recovered using a minimum mean-squared error beamformer approach without any hypothetical limitation. We apply two classes of data (speech signals and biomedical signals) to substantiate the effectiveness of the proposed algorithm for underdetermined blind source separation. Springer Science+Business Media New York, 2015 Underdetermined blind source separation nationallicence Hierarchical alternating least squares nationallicence Canonical decomposition nationallicence Minimum mean-squared error beamformer nationallicence Ge Sunan Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116023, Dalian, China aut Han Jie Department of Neurology, First Affiliated Hospital of Dalian Medical University, 116044, Dalian, China aut Han Min Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116023, Dalian, China aut Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2935-2950 0278-081X 34:9<2935 2015 34 34 https://doi.org/10.1007/s00034-015-9969-8 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00034-015-9969-8 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ge Sunan Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116023, Dalian, China aut NATIONALLICENCE 700 1- Han Jie Department of Neurology, First Affiliated Hospital of Dalian Medical University, 116044, Dalian, China aut NATIONALLICENCE 700 1- Han Min Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, 116023, Dalian, China aut NATIONALLICENCE 773 0- Circuits, Systems, and Signal Processing Springer US; http://www.springer-ny.com 34/9(2015-09-01), 2935-2950 0278-081X 34:9<2935 2015 34 34