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Multichannel MMSE Wiener Filter Using Complex Real and Imaginary Spectral Coefficients for Distributed Microphone Speech Enhancement

Received: 26 October 2016     Accepted: 26 November 2016     Published: 20 December 2016
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Abstract

In this paper, the authors propose a frequency domain multichannel Wiener filter for distributed microphone speech enhancement using acoustic arrays. The current state-of-the-art single channel estimators achieve noticeable performance gains using the to-noise ratio (SNR) and segmental signal-to-noise ratio (SSNR) objective measures, which measure noise reduction, but only achieve marginal performance gains using the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) objective metrics, which correlate better than SNR and SSNR with speech distortion and overall speech quality. By extending the traditional single channel Wiener filter to multiple distributed channels through minimum mean-square error (MMSE) estimation of the complex real and imaginary components, the approach presented here demonstrates increases in the SSNR, LLR, and PESQ objective measures. Experimental results show that the new multichannel Wiener filter using distributed microphones produces gains of 5.0 dB (SSNR improvement), 0.7 (LLR output), and 0.8 (PESQ output) averaged across the 0 dB, 5 dB, and 10 dB input SNRs over the baseline single channel Wiener filter.

Published in International Journal of Theoretical and Applied Mathematics (Volume 2, Issue 2)
DOI 10.11648/j.ijtam.20160202.23
Page(s) 115-120
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Acoustic Arrays, Speech Enhancement, Parameter Estimation

References
[1] N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications. New York: Wiley, 1949.
[2] R. Martin, "Speech Enhancement Based on Minimum Mean-Square Error Estimation and Supergaussian Priors," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 13, pp. 845-856, 2005.
[3] Y. Ephraim and D. Malah, "Speech Enhancement Using a Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator," in IEEE Transactions on Acoustics, Speech and Signal Processing, vol. ASSP-32. New York, NY, 1984, pp. 1109-1121.
[4] Y. Ephraim and D. Malah, "Speech Enhancement using a Minimum Mean-Square Error Log-Spectral Amplitude Estimator," in IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 33. New York, NY, 1985, pp. 443-445.
[5] P. C. Loizou, "Speech Enhancement Based on Perceptually Motivated Bayesian Estimators of the Magnitude Spectrum," IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 13, pp. 857-869, 2005.
[6] I. Andrianakis and P. R. White, "Speech Spectral Amplitude Estimators using Optimally-Shaped Gamma and Chi Priors," Speech Communication, pp. 1-14, 2009.
[7] J. S. Erkelens, R. C. Hendriks, R. Heusdens, and J. Jensen, "Minimum Mean-Square Error Estimation of Discrete Fourier Coefficients with Generalized Gamma Priors," IEEE Transactions on Audio, Speech, and Language Processing, vol. 15, pp. 1741-1752, 2007.
[8] E. Plourde and B. Champagne, "Auditory-Based Spectral Amplitude Estimators for Speech Enhancement," IEEE Transactions on Audio, Speech and Language Processing, vol. 16, pp. 1614-1623, 2008.
[9] C. H. You, S. N. Koh, and S. Rahardja, "Beta-Order MMSE Spectral Amplitude Estimation for Speech Enhancement," IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 13, 2005.
[10] Y. Hu and P. Loizou, "Evaluation of Objective Quality Measures for Speech Enhancement," IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, pp. 229-238, 2008.
[11] J. Polastre, R. Szewczyk, and A. Mainwaring, "Chapter 18: Analysis of Wireless Sensor Networks for Habitat Monitoring," in Wireless Sensor Networks. Norwell, MA: Kluwer Academic Publishers, 2004.
[12] R. C. Hendriks, R. Heusdens, U. Kjerns, and J. Jensen, "On Optimal Multichannel Mean-Squared Error Estimators for Speech Enhancement," IEEE Signal Processing Letters, vol. 16, pp. 885-888, 2009.
[13] M. B. Trawicki, "Distributed Multichannel Processing for Signal Enhancement," in Electrical and Computer Engineering. Milwaukee: Marquette University, 2009, pp. 228.
[14] B. Widrow, J. R. Glover, Jr., J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearn, J. R. Zeidler, E. Dong, Jr., and R. C. Goodlin, "Adaptive Noise Cancelling: Principles and Applications," Proceedings of the IEEE, vol. 63, pp. 1692-1716, 1975.
[15] S. Doclo and M. Moonen, "GSVD-Based Optimal Filtering for Single and Multimicrophone Speech Enhancement," in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 50. New York, NY, 2002, pp. 2230-2244.
[16] S. Doclo and M. Moonen, "On the Output SNR of the Speech-Distortion Weighted Multichannel Wiener Filter," IEEE Signal Processing Letters, vol. 12, pp. 809-811, 2005.
[17] A. Spriet, M. Moonen, and J. Wouters, "Spatially Pre-Processed Speech Distortion Weighted Multi Channel Wiener Filtering for Noise Reduction," Signal Processing, vol. 84, pp. 2367-2387, 2004.
[18] M. Brandstein and D. Ward, Microphone Arrays. New York, NY: Springer-Verlag, 2001.
[19] I. A. McCowan, "Robust Speech Recognition using Microphone Arrays." Brisbane, Australia: Queensland University of Technology, 2001.
[20] C. H. Knapp and G. C. Carter, "The Generalized Correlation Method for Estimation of Time Delay," in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-24. New York, NY, 1976, pp. 320-327.
[21] T. Lotter, C. Benien, and P. Vary, "Multichannel Direction-Independent Speech Enhancement Using Spectral Amplitude Estimation," in EURASIP Journal on Applied Signal Processing. New York, NY, 2003, pp. 1147-1156.
[22] I. S. Gradshteyn and Z. M. Ryzhik, Table of Integrals, Series, and Products, 5th Edition ed. New York City, NY: Academic, 1994.
[23] J. Garofolo, L. Lamel, and W. Fisher, "TIMIT Acoustic-Phonetic Continuous Speech Corpus." Gaithersburg, MD: Linguistic Data Consortium, 1993.
[24] A. Varga and H. J. M. Steeneken, "Assessment for Automatic Speech Recognition: II. NOISEX-92: A Database and an Experiment to Study the Effect of Additive Noise on Speech Recognition Systems," Speech Communication, vol. 12, pp. 247-251, 1993.
[25] M. B. Trawicki and M. T. Johnson, "Optimal Distributed Microphone Phase Estimation," presented at International Conference on Acoustics, Speech, and Signal Processing, Taipei, Taiwan, R. O. C., 2009.
[26] P. E. Papamichalis, Practical Approaches to Speech Coding. New York, NY: Prentice-Hall, 1987.
[27] S. R. Quackenbush, I. T. P. Barnwell, and M. A. Clements, Objective Measures of Speech Quality. New York: Prentice-Hall, 1998.
[28] ITU-T, "Recommendation P.862: Perceptual Evaluation of Speech Quality (PESQ), an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs," 2001.
Cite This Article
  • APA Style

    Marek B. Trawicki, Michael T. Johnson. (2016). Multichannel MMSE Wiener Filter Using Complex Real and Imaginary Spectral Coefficients for Distributed Microphone Speech Enhancement. International Journal of Theoretical and Applied Mathematics, 2(2), 115-120. https://doi.org/10.11648/j.ijtam.20160202.23

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    ACS Style

    Marek B. Trawicki; Michael T. Johnson. Multichannel MMSE Wiener Filter Using Complex Real and Imaginary Spectral Coefficients for Distributed Microphone Speech Enhancement. Int. J. Theor. Appl. Math. 2016, 2(2), 115-120. doi: 10.11648/j.ijtam.20160202.23

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    AMA Style

    Marek B. Trawicki, Michael T. Johnson. Multichannel MMSE Wiener Filter Using Complex Real and Imaginary Spectral Coefficients for Distributed Microphone Speech Enhancement. Int J Theor Appl Math. 2016;2(2):115-120. doi: 10.11648/j.ijtam.20160202.23

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  • @article{10.11648/j.ijtam.20160202.23,
      author = {Marek B. Trawicki and Michael T. Johnson},
      title = {Multichannel MMSE Wiener Filter Using Complex Real and Imaginary Spectral Coefficients for Distributed Microphone Speech Enhancement},
      journal = {International Journal of Theoretical and Applied Mathematics},
      volume = {2},
      number = {2},
      pages = {115-120},
      doi = {10.11648/j.ijtam.20160202.23},
      url = {https://doi.org/10.11648/j.ijtam.20160202.23},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijtam.20160202.23},
      abstract = {In this paper, the authors propose a frequency domain multichannel Wiener filter for distributed microphone speech enhancement using acoustic arrays. The current state-of-the-art single channel estimators achieve noticeable performance gains using the to-noise ratio (SNR) and segmental signal-to-noise ratio (SSNR) objective measures, which measure noise reduction, but only achieve marginal performance gains using the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) objective metrics, which correlate better than SNR and SSNR with speech distortion and overall speech quality. By extending the traditional single channel Wiener filter to multiple distributed channels through minimum mean-square error (MMSE) estimation of the complex real and imaginary components, the approach presented here demonstrates increases in the SSNR, LLR, and PESQ objective measures. Experimental results show that the new multichannel Wiener filter using distributed microphones produces gains of 5.0 dB (SSNR improvement), 0.7 (LLR output), and 0.8 (PESQ output) averaged across the 0 dB, 5 dB, and 10 dB input SNRs over the baseline single channel Wiener filter.},
     year = {2016}
    }
    

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    AU  - Marek B. Trawicki
    AU  - Michael T. Johnson
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    AB  - In this paper, the authors propose a frequency domain multichannel Wiener filter for distributed microphone speech enhancement using acoustic arrays. The current state-of-the-art single channel estimators achieve noticeable performance gains using the to-noise ratio (SNR) and segmental signal-to-noise ratio (SSNR) objective measures, which measure noise reduction, but only achieve marginal performance gains using the Log-Likelihood Ratio (LLR) and Perceptual Evaluation of Speech Quality (PESQ) objective metrics, which correlate better than SNR and SSNR with speech distortion and overall speech quality. By extending the traditional single channel Wiener filter to multiple distributed channels through minimum mean-square error (MMSE) estimation of the complex real and imaginary components, the approach presented here demonstrates increases in the SSNR, LLR, and PESQ objective measures. Experimental results show that the new multichannel Wiener filter using distributed microphones produces gains of 5.0 dB (SSNR improvement), 0.7 (LLR output), and 0.8 (PESQ output) averaged across the 0 dB, 5 dB, and 10 dB input SNRs over the baseline single channel Wiener filter.
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Author Information
  • Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

  • Department of Electrical and Computer Engineering, Marquette University, Milwaukee, USA

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