Study and Application on the Single-channel Signal Separation by OFMM

A method called the optimal frequency matching method is described which can separate multiple components from a single-channel signal. The principle and its signal separation method introduce, characteristics and advantages of this method are summarized. Take acoustic CT detection signal, natural sound and mechanical vibration signal as examples, the process of the optimal frequency matching method in signal separations has been demonstrated in detail. The rationality and effectiveness of these separated signals have been also verified with the digital audio technology. It is shown that a variety of mixed signals can be separated with very consistent degree of their integrity by the optimal frequency matching method.

[1]  Li Wang,et al.  Foundation and Application of Fluid End’s Vibration Model for Drilling Reciprocation Pump , 2011 .

[2]  P. Rayner,et al.  SINGLE CHANNEL SIGNAL SEPARATION USING LINEAR TIME VARYING FILTERS : SEPARABILITY OF NON-STATIONARY STOCHASTIC SIGNALS , 1998 .

[3]  Wenfeng Wu,et al.  Blind Source Separation of Single-channel Mechanical Signal Based on Empirical Mode Decomposition , 2011 .

[4]  Rémi Gribonval,et al.  Sparse Representations in Audio and Music: From Coding to Source Separation , 2010, Proceedings of the IEEE.

[5]  Jingjing Du,et al.  Mixing vector estimation in single channel blind source separation of angle modulated signal sources based on cumulant system of equations , 2009, Signal Process..

[6]  Tuomas Virtanen,et al.  Monaural Sound Source Separation by Nonnegative Matrix Factorization With Temporal Continuity and Sparseness Criteria , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Daniel P. W. Ellis,et al.  Speech separation using speaker-adapted eigenvoice speech models , 2010, Comput. Speech Lang..

[8]  Lan Ying.  The separability of active sonar signals in the presence of reverberation , 2010 .

[9]  Chang Dong Yoo,et al.  Underdetermined Blind Source Separation Based on Subspace Representation , 2009, IEEE Transactions on Signal Processing.

[10]  Ming Liang,et al.  Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition , 2007 .

[11]  Te-Won Lee,et al.  A subspace approach to single channel signal separation using maximum likelihood weighting filters , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Li Mei.  Modelling and Separation Processing of the Power Interference Vibration Signals , 2012 .

[13]  Gang Liu,et al.  A novel blind source separation method for single-channel signal , 2010, Signal Process..