Applying the Accumulation of Cross-Power Spectrum Technique for Traditional Generalized Cross-Correlation Time Delay Estimation

In many real time applications, time delay estimation requires a special solution. Despite the various approaches, which were proposed over the years, the topic remains hot for digital signal processing because of its large field of applications and implementation forms. Among different classes of methods for this issue, general crosscorrelation method is wildly used. It offers good results and does not need an adaptation time, like those based on adaptive filtering. In this paper, we make a survey and compare the most popular generalized cross-correlation methods. We extend the analysis, by applying the accumulation of crosspower spectrum technique, for all well known generalized cross-correlation methods. The comparisons are provided by detailed numerical and simulation analysis, using several metrics. Based on the accuracy rate, error rate, standard deviation of relative error and computing time we provide new considerations for traditional generalized cross-correlation methods.

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

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

[3]  I. Claesson,et al.  Comparison between whitened generalized cross correlation and adaptive filter for time delay estimation with scattered arrays for passive positioning of moving targets in Baltic Sea shallow waters , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[4]  Hongyu Wang,et al.  An Eckart-weighted adaptive time delay estimation method , 1996, IEEE Trans. Signal Process..

[5]  Trevor Darrell,et al.  Learning a Precedence Effect-Like Weighting Function for the Generalized Cross-Correlation Framework , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  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.

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

[8]  N. Ahmed,et al.  On the Roth and SCOT algorithms: Time-domain implementations , 1983, Proceedings of the IEEE.

[9]  Horia Cucu,et al.  Extensive evaluation experiments for the accumulated cross-power spectrum methods for time delay estimation , 2013, 2013 7th Conference on Speech Technology and Human - Computer Dialogue (SpeD).

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

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

[12]  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.

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

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

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

[16]  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).

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

[18]  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.

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

[20]  Maurizio Omologo,et al.  Use of the crosspower-spectrum phase in acoustic event location , 1997, IEEE Trans. Speech Audio Process..

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

[22]  Tianshuang Qiu,et al.  The SCOT weighted adaptive time delay estimation algorithm based on minimum dispersion criterion , 2010, 2010 International Conference on Intelligent Control and Information Processing.

[23]  Maurizio Omologo,et al.  Acoustic event localization using a crosspower-spectrum phase based technique , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

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

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

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

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

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

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