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.


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


FastICA; KernSmooth
Full Text: DOI


[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)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.