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Discrete Space-Time Filtering

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Fundamentals of Adaptive Signal Processing

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

In many scientific and technological areas, acquiring signals relating to the same stochastic process, with a multiplicity of homogeneous sensors and arranged in different spatial positions, is sometimes necessary or simply useful. For example, this is the case of the acquisition of biomedical signals, such as electroencephalogram (EEG), electrocardiogram (ECG), and tomography or of telecommunications signals such as those deriving from the antenna arrays and radars, the detection of seismic signals, the sonar, and the microphone arrays for the acquisition of acoustic signals. The phenomena measured in these applications may have different physical nature but, in any case, the array of sensors, or receivers, is made to acquire processes concerning the propagation of electromagnetic or mechanical waves coming from one or more radiation sources.

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Notes

  1. 1.

    In [9], the development is carried out in the case of anechoic model for which the matrix A is the steering matrix defined in (9.24). Here, the proposed study model is more general and also valid for reverberant propagation environments.

  2. 2.

    Note that for x∈(ℝ,ℂ)P×1, we have \( \operatorname{tr}\left[E\left\{\mathbf{x}{\mathbf{x}}^T\right\}/\left({\scriptscriptstyle \frac{{\mathbf{x}}^T\mathbf{x}}{P}}\right)\right]\sim P \).

References

  1. Van Trees HL (2002) Optimum array processing: part IV of detection, estimation, and modulation theory. Wiley Interscience, New York, ISBN 0-471-22110-4

    Google Scholar 

  2. McCowan IA (2001) report. Queensland University Technology, Australia

    Google Scholar 

  3. Brandstein M, Wards D (2001) Microphone arrays. Springer, Berlin

    Book  Google Scholar 

  4. Applebaum SP (1966) Adaptive arrays. Syracuse University Research Corp, Report SURC SPL TR 66-001 (reprinted in IEEE Trans on AP, vol AP-24, pp 585–598, Sept 1976)

    Google Scholar 

  5. Johnson D, Dudgeon D (1993) Array signal processing: concepts and techniques. Prentice-Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  6. Haykin S, Ray Liu KJ (2009) Handbook on array processing and sensor networks. Wiley, New York. ISBN 978-0-470-37176-3

    Google Scholar 

  7. Seto WW (1971) Acoustics. McGraw-Hill, New York

    Google Scholar 

  8. Van Veen B, Buckley KM (1988) Beamforming a versatile approach to spatial filtering. IEEE Signal Process Mag 5(2):4–24

    Google Scholar 

  9. Fisher S, Kammeyer KD, Simmer KU (1996) Adaptive microphone arrays for speech enhancement in coherent and incoherent noise fields. In: 3rd Joint meeting of the acoustical society of America and the acoustical society of Japan, Honolulu, Hawaii, 2–6 Dec 1996

    Google Scholar 

  10. Krim H, Viberg M (1996) Two decades of array signal processing research—the parametric approach. IEEE Signal Process Mag 13(4):67–94

    Article  Google Scholar 

  11. Elko GW (2001) Spatial coherence functions for differential microphones in isotropic fields. In: Brandstein M, Ward D (eds) Microphone arrays. Springer, Heidelberg, pp 61–85. ISBN 3-540-41953-5

    Chapter  Google Scholar 

  12. Doclo S, Moonen M (2007) Superdirective beamforming robust against microphone mismatch. IEEE Trans Audio Speech Lang Process 15(2):617–631

    Article  Google Scholar 

  13. Cox H, Zeskind RM, Kooij T (1986) Practical supergain. IEEE Trans ASSP ASSP-34(3): 393–398

    Article  Google Scholar 

  14. Elko GW (2004) Differential microphones array. In: Huang Y, Benesty J (eds) Audio signal processing for next generation multimedia communication systems. Kluwer Academic, Dordrecht. ISBN 1-4020-7768-8

    Google Scholar 

  15. Kolundžija M, Faller C, Vetterli M (2011) Spatiotemporal gradient analysis of differential microphone arrays. J Audio Eng Soc 59(1/2):20–28

    Google Scholar 

  16. Buck M, Rößler M (2001) First order differential microphone arrays for automotive applications. In: International workshop on acoustic echo and noise control, Darmstadt, Germany, pp 19–22

    Google Scholar 

  17. Buck M (2002) Aspects of first-order differential microphone arrays in the presence of sensor imperfections. Eur Trans Telecommun 13(2):115–122

    Article  Google Scholar 

  18. Benesty J, Chen J, Huang Y, Dmochowski J (2007) On microphone-array beamforming from a mimo acoustic signal processing perspective. IEEE Trans Audio Speech Lang Process 15(3):1053–1065

    Article  Google Scholar 

  19. Harris FJ (1978) On the use of windows for harmonic analysis with the discrete Fourier transform. Proc IEEE 66(1):51–84

    Article  Google Scholar 

  20. Doclo S, Moonen M (2003) Design of broadband beamformers robust against gain and phase errors in the microphone array characteristics. IEEE Trans Signal Process 51(10):2511–2526

    Article  Google Scholar 

  21. Chen H, Ser W (2009) Design of robust broadband beamformers with passband shaping characteristics using Tikhonov regularization. IEEE Trans Audio Speech Lang Proc 17(4):665–681

    Article  Google Scholar 

  22. Capon J, Greenfield RJ, Kolker RJ (1967) Multidimensional maximum-likelihood processing of a large aperture seismic array. Proc IEEE 55:192–211

    Article  Google Scholar 

  23. Capon J (1969) High resolution frequency-wavenumber spectrum analysis. Proc IEEE 57:1408–1418

    Article  Google Scholar 

  24. Cox H, Zeskind RM, Owen MM (1987) Robust adaptive beamforming. IEEE Trans ASSP ASSP-35:1365–1375

    Article  Google Scholar 

  25. Bitzer J, Kammeyer KD, Simmer KU (1999) An alternative implementation of the superdirective beamformer. In: Proceedings of 1999 I.E. workshop on applications of signal processing to audio and acoustics, New Paltz, New York

    Google Scholar 

  26. Trucco A, Traverso F, Crocco M (2013) Robust superdirective end-fire arrays. MTS/IEEE Oceans. doi: 10.1109/OCEANS-Bergen.2013.6607994

  27. Zelinski R (1988) A microphone array with adaptive post-filtering for noise reduction in reverberant rooms. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, ICASSP-88, vol 5, pp 2578–2581

    Google Scholar 

  28. Marro C, Mahieux Y, Simmer K (1996) Performance of adaptive dereverberation techniques using directivity controlled arrays. In: Proceedings of European signal processing conference EUSIPCO96, Trieste, Italy, pp 1127–1130

    Google Scholar 

  29. Fisher S, Kammeyer KD (1997) Broadband beamforming with adaptive postfiltering for speech acquisition in noisy environments. In: Proceedings of IEEE international conference on acoustics, speech, and signal processing, ICASSP-97, vol 1, pp 359–362, April 1997

    Google Scholar 

  30. Frost OL III (1972) An algorithm for linearly constrained adaptive array processing. Proc IEEE 60(8):926–935

    Article  Google Scholar 

  31. Buckley KM, Griffiths LJ (1986) An adaptive generalized sidelobe canceller with derivative constrains. IEEE Trans Antenn Propag AP34(3):311–319

    Article  Google Scholar 

  32. Er MH, Cantoni A (1983) Derivative constraints for broad-band element space antenna array processors. IEEE Trans Antenn Propag AP31:1378–1393

    Google Scholar 

  33. Karray L, Martin A (2003) Toward improving speech detection robustness for speech recognition in adverse environments. Speech Commun 3:261–276

    Article  Google Scholar 

  34. Mousazadeh S, Cohen I (2013) Voice activity detection in presence of transient noise using spectral clustering. IEEE Trans Audio Speech Lang Process 21(6):1261–1271

    Article  Google Scholar 

  35. Joho M, Moschytz GS (1997) Adaptive beamforming with partitioned frequency-domain filters. In: IEEE workshop on applications of signal processing to audio and acoustics, New Palz, NY, USA, 19–22 Oct 1997

    Google Scholar 

  36. Bitzer J, Kammeyer KD, Simmer KU (1999) An alternative implementation of the superdirective beamformer. In: Proceedings of 1999 I.E. workshop on applications of signal processing to audio and acoustics, New Paltz, New York, 17–20 Oct 1999

    Google Scholar 

  37. Fischer S, Simmer KU (1996) Beamforming microphone arrays for speech acquisition in noisy environments. Speech Commun 20(3–4):215–227 (special issue on acoustic echo control and speech enhancement techniques)

    Article  Google Scholar 

  38. Scarbrough K, Ahmed N, Carter GC (1981) On the simulation of a class of time delay estimation algorithms. IEEE Trans Acoust Speech Signal Process ASSP-29(3):534–540

    Article  Google Scholar 

  39. Schmidt RO (1981) A signal subspace approach to multiple emitter location and spectral estimation. PhD dissertation, Stanford University, Stanford, CA

    Google Scholar 

  40. Li T, Nehorai A (2011) Maximum likelihood direction finding in spatially colored noise fields using sparse sensor arrays. IEEE Trans Signal Process 59:1048–1062

    Article  MathSciNet  Google Scholar 

  41. Schmidt RO (1986) Multiple emitter location and signal parameter estimation. IEEE Trans Antenn Propag 34(3):276–280

    Article  Google Scholar 

  42. Stoica P, Nehorai A (1989) MUSIC, maximum likelihood and Cramér-Rao bound. IEEE Trans Acoust Speech Signal Process 37:720–741

    Article  MathSciNet  MATH  Google Scholar 

  43. Paulraj A, Roy R, Kailath T (1985) Estimation of signal parameters via rotational invariance techniques-ESPRIT. In: Proceedings of 19th Asilomar conference on signals systems, and computers. Asilomar, Pacific Grove, CA

    Google Scholar 

  44. Roy R, Kailath T (1989) ESPRIT—estimation of signal parameters via rotational invariance techniques. IEEE Trans Acoust Speech Signal Process 37(7):984–995

    Article  Google Scholar 

  45. Special Issue (1981) Time delay estimation. IEEE Trans Acoust Speech Signal Process ASSP-29(3)

    Google Scholar 

  46. Knapp CH, Carter GC (1976) The generalized correlation method for estimation of time delay. IEEE Trans Acoust Speech Signal Process ASSP-24(4):320–327

    Article  Google Scholar 

  47. Di Biase JH, Silverman HF, Brandstein MS (2001) Robust localization in reverberant rooms. In: Brandstein M, Ward D (eds) Microphone arrays: signal processing techniques and applications. Springer, Berlin. ISBN 3-540-42953-5

    Google Scholar 

  48. Huang Y, Benesty J, Chen J (2008) Time delay estimation and source localization. In: Springer handbook of speech processing. Springer, Berlin, ISBN: 978-3-540-49125-5

    Google Scholar 

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  1. DIET, Sapienza University of Rome, Rome, Italy

    Aurelio Uncini

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Uncini, A. (2015). Discrete Space-Time Filtering. In: Fundamentals of Adaptive Signal Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-02807-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-02807-1_9

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