A novel blind source separation method for single-channel signal

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.

[1]  E. Oja,et al.  Independent Component Analysis , 2001 .

[2]  G.-J. Jang,et al.  Single-channel signal separation using time-domain basis functions , 2003, IEEE Signal Processing Letters.

[3]  R. Lambert Multichannel blind deconvolution: FIR matrix algebra and separation of multipath mixtures , 1996 .

[4]  Gordon R. J. Cooper,et al.  Comparing time series using wavelet-based semblance analysis , 2008, Comput. Geosci..

[5]  H. Stanley,et al.  Scale invariance in the nonstationarity of human heart rate. , 2000, Physical review letters.

[6]  Paris Smaragdis,et al.  Mitsubishi Electric Research Laboratories , 1994 .

[7]  John R. Hershey,et al.  Super-human multi-talker speech recognition: the IBM 2006 speech separation challenge system , 2006, INTERSPEECH.

[8]  Walter Kellermann,et al.  A real-time blind source separation scheme and its application to reverberant and noisy acoustic environments , 2006, Signal Process..

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[11]  Maria Papadopouli,et al.  Singular spectrum analysis of traffic workload in a large-scale wireless lan , 2007, MSWiM '07.

[12]  Dinh-Tuan Pham,et al.  Mutual information approach to blind separation of stationary sources , 2002, IEEE Trans. Inf. Theory.

[13]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[14]  F. Takens Detecting strange attractors in turbulence , 1981 .

[15]  John W. Betz,et al.  Binary Offset Carrier Modulations for Radionavigation , 2001 .

[16]  Michael I. Jordan,et al.  Blind One-microphone Speech Separation: A Spectral Learning Approach , 2004, NIPS.

[17]  Nina Golyandina,et al.  Automatic extraction and forecast of time series cyclic components within the framework of SSA , 2005 .

[18]  Gabriel Rilling,et al.  On empirical mode decomposition and its algorithms , 2003 .

[19]  Richard J. Povinelli,et al.  Statistical models of reconstructed phase spaces for signal classification , 2006, IEEE Transactions on Signal Processing.

[20]  Sam T. Roweis,et al.  One Microphone Source Separation , 2000, NIPS.

[21]  Richard M. Everson,et al.  Independent Component Analysis: Principles and Practice , 2001 .

[22]  L. A. Nunes Amaral,et al.  Heuristic segmentation of a nonstationary time series. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.