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A novel blind source separation method for single-channel signal. (English) Zbl 1197.94087

Summary: The blind separation of single-channel signal is one of the most important aspects in many fields. Our research is carried out to develop a blind separation method of single-channel signal, in which the singular spectrum analysis (SSA) and blind source separation (BSS) techniques are jointly used, i.e. the single-channel signal is firstly changed into pseudo-MIMO (multi-input and multi-output) mode, and then each source signal is separated via a fast BSS algorithm. A signal preprocessing procedure, which is mainly focused on testing the nonstationarity of single-channel signal, is conducted before the operations of mixed signal transform and separation. In this research, the approach of heuristic segmentation of a nonstationary time-series is proposed. Throughout the experiment, the effectiveness of the proposed method is validated with a data set taken from a digital wideband receiver in an outdoor test. Then, a comparison is made between the proposed method and the Hilbert-Huang transform (HHT)-based signal separation method. The advantage of the proposed method is exhibited.

MSC:

94A12 Signal theory (characterization, reconstruction, filtering, etc.)

Software:

FastICA; KernSmooth
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References:

[1] T. Alexandrov, N. Golyandina, Automatic extraction and forecast of time-series cyclic components within the framework of SSA, in: Proceedings of the Fifth Workshop on Simulation, St. Petersburg, Russia, June 26–July 2, 2005, pp. 45–50.
[2] G. Tzagkarakis, M. Papadopouli, P. Tsakalides, Singular spectrum analysis of traffic workload in a large-scale wireless LAN, in: CDROM of Proceedings MSWIM’07, Chania, Crete Island, Greece,October 22–26, 2007.
[3] Huang, Norden E.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time-series analysis, Royal society Proceedings on mathematical, physical, and engineering sciences 454, No. 197, 903-995 (1998) · Zbl 0945.62093
[4] G. Rilling, P. Flandrin, et al., On empirical mode decomposition and its algorithms, in: Proceedings of the IEEE–EURASIP Workshop on Nonlinear Signal and Image Processing, NSIP-03, Grado, Italy, June 8–11, 2003. · Zbl 1390.94382
[5] S.T. Roweis, One microphone source separation, Advances in Neural Information Processing Systems, 2000, pp. 793–799.
[6] T. Kristjansson, J. Hershey, P. Olsen, S. Rennie, R. Gopinath, Super-human multi-talker speech recognition: the IBM2006 speech separation challenge system, in: Proceedings of the International Conference on Spoken Language Processing (INTERSPEECH), Pittsburgh, Pennsylvania, 2006, pp. 97–100.
[7] F.R. Bach, M.I. Jordan, Blind one-microphone speech separation: a spectral learning approach, Advances in Neural Information Processing Systems, 2005, pp. 65–72.
[8] Jang, G. J.; Lee, T. W.: Single-channel source separation using time-domain basis funtions, IEEE transactions on signal processing 10, No. 6, 168-171 (2003)
[9] P. Smaragdis, Discovering auditory objects through non-negativity constraints, in: CDROM of Proceedings of the Statistical and Perceptual Audio Processing (SAPA), Jeju, Korea, 2004.
[10] Fukuda, K.; Stanley, H. Eugene; Amaral, Luis A. Nunes: Heuristic segmentation of a nonstationary time-series, Physical review E 69, 021108 (2004)
[11] Bernaola-Galván, P.; Ivanov, P. Ch.; Amaral, L. A. N.; Stanley, H. E.: Scale invariance in the nonstationarity of human heart, Physical review letters 87, 168105 (2001)
[12] Betz, J.: Contrib title: binary offset carrier modulations for radionavigation, Journal of the institute of navigation 48, 227-246 (2001)
[13] R. Lambert, Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures, Ph.D. Dissertation, Department of Electrical Engineering, University of Southern California, May 1996.
[14] Hyvärinen, A.; Karhunen, J.; Oja, E.: Independent component analysis, (2001)
[15] Roberts, S.; Everson, R.: Independent components analysis: principles and practice, (2001) · Zbl 0979.62043
[16] Vautard, R.; Yiou, P.; Ghil, M.: Singular-spectrum analysis: a toolkit for short, noisy chaotic signals, Physica D 58, No. 1–4, 95-126 (1992)
[17] Takens, F.: Detecting strange attractors in turbulence, Dynamical systems and turbulence, warwick, 1980, lecture notes in mathematics 898, 361-381 (1981) · Zbl 0513.58032
[18] Povinelli, R. J.; Johson, M. T.: Statistical models of reconstructed phase spaces for signal classification, IEEE transactions on signal processing 54, No. 6, 2178-2186 (2006) · Zbl 1374.94584
[19] Hayes, M. H.: Statistical digital signal processing and modeling, (1996)
[20] Aichner, Robert; Buchner, Herbert; Yan, Fei; Kellermann, Walter: A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments, Signal processing 86, 1260-1277 (2006) · Zbl 1163.94311 · doi:10.1016/j.sigpro.2005.06.022
[21] Pham, D. T.: Mutual information approach to blind separation of stationary sourcs, IEEE transactions on information theory 48, 1935-1946 (2002) · Zbl 1061.94514 · doi:10.1109/TIT.2002.1013134
[22] Wand, M. P.; Jones, M. C.: Kernel smoothing, (1995) · Zbl 0854.62043
[23] Cooper, G. R. J.; Cowan, D. R.: Comparing time-series using wavelet-based semblance analysis, Computer geosciences 34, 95-102 (2008)
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