Extensive evaluation experiments for the accumulated cross-power spectrum methods for time delay estimation

In numerous real time signal processing applications, time delay estimation (TDE) is still a hot topic. A recently proposed solution, based on Generalized Cross Correlation (GCC) method, is evaluated further in this paper. Experimental results show that among traditional variants of GCC methods the accumulated p-cross power spectrum phase proposed by us is the most accurate. Another important aspect is that the computing time of this method is comparable with those of other popular GCC methods.

[1]  Bernard Widrow,et al.  Adaptive Signal Processing , 1985 .

[2]  Hong Liu,et al.  A modified cross power-spectrum phase method based on microphone array for acoustic source localization , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[3]  Horia Cucu,et al.  New Considerations for Accumulated ρ-Cross Power Spectrum Phase with Coherence Time Delay Estimation , 2012 .

[4]  Roman A. Dyba.  Parallel structures for fast estimation of echo path pure delay and their applications to sparse echo cancellers , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[5]  Hongyang Deng,et al.  Partial update PNLMS algorithm for network echo cancellation , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  E. Hannan,et al.  Delay estimation and the estimation of coherence and phase , 1981 .

[7]  Carl Eckart.  Optimal Rectifier Systems for the Detection of Steady Signals , 1952 .

[8]  Alfred O. Hero,et al.  A new generalized cross correlator , 1985, IEEE Trans. Acoust. Speech Signal Process..

[9]  Benesty,et al.  Adaptive eigenvalue decomposition algorithm for passive acoustic source localization , 2000, The Journal of the Acoustical Society of America.

[10]  Horia Cucu,et al.  Fast accurate time delay estimation based on enhanced accumulated Cross-power Spectrum Phase , 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

[11]  Hongyang Deng,et al.  Efficient partial update algorithm based on coefficient block for sparse impulse response identification , 2008, 2008 42nd Annual Conference on Information Sciences and Systems.

[12]  E. J. Hannan,et al.  The estimation of coherence and group delay , 1971 .

[13]  Piergiorgio Svaizer,et al.  Efficient Time Delay Estimation based on Cross-Power Spectrum Phase , 2006, 2006 14th European Signal Processing Conference.

[14]  Shiunn-Jang Chern,et al.  A new adaptive constrained LMS time delay estimation algorithm , 1998, Signal Process..

[15]  Andy W. H. Khong,et al.  Efficient Use Of Sparse Adaptive Filters , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[16]  James L. Flanagan,et al.  A DSP implementation of source location using microphone arrays. , 1996 .

[17]  Peter R. Roth,et al.  Effective measurements using digital signal analysis , 1971, IEEE Spectrum.

[18]  R. Boucher,et al.  Performance of the generalized cross correlator in the presence of a strong spectral peak in the signal , 1981 .

[19]  J. Hassab,et al.  Optimum estimation of time delay by a generalized correlator , 1979 .

[20]  Kirill Sakhnov,et al.  Echo Delay Estimation Using Algorithms Based on Cross-correlation , 2011 .

[21]  G. Carter,et al.  The generalized correlation method for estimation of time delay , 1976 .

[22]  Joseph C. Hassab,et al.  A quantitative study of optimum and sub-optimum filters in the generalized correlator , 1979, ICASSP.

[23]  G. C. Carter,et al.  The smoothed coherence transform , 1973 .