New Considerations for Accumulated ρ-Cross Power Spectrum Phase with Coherence Time Delay Estimation

Time delay estimation (TDE) has many applications in a variety of digital signal applications. The main issues today are more or less application-dependent, because every specific utilization scenario has its own demands (related to accuracy, computational load, etc.) As a solution for this topic, in this paper we continue the evaluation of the recently proposed accumulated ρ-cross power spectrum with coherence TDE method. The experimental results confirm that the method is faster and more accurate than the previous separated variants. Another key finding is that the TDE based on accumulation of cross-power spectrum is at least two times more accurate as the TDE based on time domain averaging. Keywords-component: Time Delay Estimation; Accumulated ρ-Cross Power Spectrum with Coherence

[1]  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.

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

[4]  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.

[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]  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).

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

[8]  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.

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

[10]  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.

[11]  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.

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

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

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

[15]  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.

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

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

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