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EURASIP Journal on Audio, Speech, and Music Processing
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Multimicrophone Speech Dereverberation: Experimental Validation

EURASIP Journal on Audio, Speech, and Music Processing volume 2007, Article number: 051831 (2007) Cite this article

Abstract

Dereverberation is required in various speech processing applications such as handsfree telephony and voice-controlled systems, especially when signals are applied that are recorded in a moderately or highly reverberant environment. In this paper, we compare a number of classical and more recently developed multimicrophone dereverberation algorithms, and validate the different algorithmic settings by means of two performance indices and a speech recognition system. It is found that some of the classical solutions obtain a moderate signal enhancement. More advanced subspace-based dereverberation techniques, on the other hand, fail to enhance the signals despite their high-computational load.

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References

  1. 1.

    Bees D, Blostein M, Kabal P: Reverberant speech enhancement using cepstral processing. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '91), May 1991, Toronto, Ontario, Canada 2: 977-980.

    Google Scholar 

  2. 2.

    Liu Q-G, Champagne B, Kabal P: A microphone array processing technique for speech enhancement in a reverberant space. Speech Communication 1996,18(4):317-334. 10.1016/0167-6393(96)00011-8

    Article  Google Scholar 

  3. 3.

    Oppenheim A, Schafer R: Digital Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1975. chapter 10

    MATH  Google Scholar 

  4. 4.

    Petropulu AP, Subramaniam S: Cepstrum based deconvolution for speech dereverberation. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '94), April 1994, Adelaide, Australia 1: 9-12.

    Google Scholar 

  5. 5.

    Allen JB, Berkley DA, Blauert J: Multimicrophone signal-processing technique to remove room reverberation from speech signals. The Journal of the Acoustical Society of America 1977,62(4):912-915. 10.1121/1.381621

    Article  Google Scholar 

  6. 6.

    Wittkop T, Hohmann V: Strategy-selective noise reduction for binaural digital hearing aids. Speech Communication 2003,39(1-2):111-138. 10.1016/S0167-6393(02)00062-6

    Article  MATH  Google Scholar 

  7. 7.

    Miyoshi M, Kaneda Y: Inverse filtering of room acoustics. IEEE Transactions on Acoustics, Speech, and Signal Processing 1988,36(2):145-152. 10.1109/29.1509

    Article  Google Scholar 

  8. 8.

    Nelson PA, Orduna-Bustamante F, Hamada H: Inverse filter design and equalization zones in multichannel sound reproduction. IEEE Transactions on Speech and Audio Processing 1995,3(3):185-192. 10.1109/89.388144

    Article  Google Scholar 

  9. 9.

    Grenier Y: A microphone array for car environments. Speech Communication 1993,12(1):25-39. 10.1016/0167-6393(93)90016-E

    MathSciNet  Article  Google Scholar 

  10. 10.

    Sydow C: Broadband beamforming for a microphone array. The Journal of the Acoustical Society of America 1994,96(2):845-849. 10.1121/1.410323

    Article  Google Scholar 

  11. 11.

    van Veen BD, Buckley KM: Beamforming: a versatile approach to spatial filtering. IEEE ASSP Magazine 1988,5(2):4-24.

    Article  Google Scholar 

  12. 12.

    Affes S, Grenier Y: A signal subspace tracking algorithm for microphone array processing of speech. IEEE Transactions on Speech and Audio Processing 1997,5(5):425-437. 10.1109/89.622565

    Article  Google Scholar 

  13. 13.

    Moulines E, Duhamel P, Cardoso J-F, Mayrargue S: Subspace methods for the blind identification of multichannel FIR filters. IEEE Transactions on Signal Processing 1995,43(2):516-525. 10.1109/78.348133

    Article  Google Scholar 

  14. 14.

    van der Veen A-J, Talwar S, Paulraj A: Blind identification of FIR channels carrying multiple finite alphabet signals. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '95), May 1995, Detroit, Mich, USA 2: 1213-1216.

    Google Scholar 

  15. 15.

    Gannot S, Moonen M: Subspace methods for multi-microphone speech dereverberation. Proceedings of the 7th IEEE/EURASIP International Workshop on Acoustic Echo and Noise Control (IWAENC '01), September 2001, Darmstadt, Germany 47-50.

    Google Scholar 

  16. 16.

    Kuttruff H: Room Acoustics. 2nd edition. Applied Science Publishers, Essex, England; 1979.

    Google Scholar 

  17. 17.

    Johnson D, Dudgeon D: Array Signal Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.

    MATH  Google Scholar 

  18. 18.

    van Gerven D, van Compernolle S, Wauters P, Verstraeten W, Eneman K, Delaet K: Multiple beam broadband beamforming: filter design and real-time implementation. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '95), October 1995, New Paltz, NY, USA 173-176.

    Google Scholar 

  19. 19.

    Giannakis GB, Halford SD: Blind fractionally spaced equalization of noisy FIR channels: direct and adaptive solutions. IEEE Transactions on Signal Processing 1997,45(9):2277-2292. 10.1109/78.622950

    Article  Google Scholar 

  20. 20.

    Eneman K, Moonen M: Ambiguity elimination in frequency-domain subspace identification. In Internal Report ESAT-SCD 06.151. Katholieke Universiteit Leuven, Leuven, Belgium; 2007:12. https://gilbert.med.kuleuven.be/~koen/reports/06-151.pdf

    Google Scholar 

  21. 21.

    Eneman K, Moonen M: DFT modulated filter bank design for oversampled subband systems. Signal Processing 2001,81(9):1947-1973. 10.1016/S0165-1684(01)00084-6

    Article  MATH  Google Scholar 

  22. 22.

    Vaidyanathan P: Multirate Systems and Filter Banks. Prentice-Hall, Englewood Cliffs, NJ, USA; 1993.

    MATH  Google Scholar 

  23. 23.

    Rahbar K, Reilly JP, Manton JH: A frequency domain approach to blind identification of MIMO FIR systems driven by quasi-stationary signals. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '02), May 2002, Orlando, Fla, USA 2: 1717-1720.

    Google Scholar 

  24. 24.

    Eneman K, Moonen M: Hybrid subband/frequency-domain adaptive systems. Signal Processing 2001,81(1):117-136. 10.1016/S0165-1684(00)00197-3

    Article  MATH  Google Scholar 

  25. 25.

    Doclo S, Moonen M: Combined frequency-domain dereverberation and noise reduction technique for multi-microphone speech enhancement. Proceedings of the 7th IEEE/EURASIP International Workshop on Acoustic Echo and Noise Control (IWAENC '01), September 2001, Darmstadt, Germany 31-34.

    Google Scholar 

  26. 26.

    Allen JB, Berkley DA: Image method for efficiently simulating small-room acoustics. The Journal of the Acoustical Society of America 1979,65(4):943-950. 10.1121/1.382599

    Article  Google Scholar 

  27. 27.

    Eneman K, Duchateau J, Moonen M, van Compernolle D, van Hamme H: Assessment of dereverberation algorithms for large vocabulary speech recognition systems. Proceedings of the 8th European Conference on Speech Communication and Technology (Eurospeech '03), September 2003, Geneva, Switzerland 2689-2692.

    Google Scholar 

  28. 28.

    Duchateau J, Demuynck K, van Compernolle D, Wambacq P: Class definition in discriminant feature analysis. Proceedings of the 7th European Conference on Speech Communication and Technology (Eurospeech '01), September 2001, Aalborg, Denmark 3: 1621-1624.

    Google Scholar 

  29. 29.

    Demuynck K, Duchateau J, van Compernolle D, Wambacq P: Improved feature decorrelation for HMM-based speech recognition. Proceedings of the 5th International Conference on Spoken Language Processing (ICSLP '98), November-December 1998, Sydney, Australia 7: 2907-2910.

    Google Scholar 

  30. 30.

    Duchateau J, Demuynck K, van Compernolle D: Fast and accurate acoustic modelling with semi-continuous HMMs. Speech Communication 1998,24(1):5-17. 10.1016/S0167-6393(98)00002-8

    Article  Google Scholar 

  31. 31.

    Demuynck K, Duchateau J, van Compernolle D, Wambacq P: An efficient search space representation for large vocabulary continuous speech recognition. Speech Communication 2000,30(1):37-53. 10.1016/S0167-6393(99)00030-8

    Article  Google Scholar 

  32. 32.

    Bees D: Enhancement of acoustically reverberant speech using cepstral methods, Ph.D. thesis. McGill University, Montreal, Canada; 1990.

    Google Scholar 

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Author information

Affiliations

  1. ExpORL, Department of Neurosciences, Katholieke Universiteit Leuven, O & N 2, Herestraat 49 bus 721, Leuven, 3000, Belgium

    Koen Eneman

  2. GroupT Leuven Engineering School, Vesaliusstraat 13, Leuven, 3000, Belgium

    Koen Eneman

  3. SCD, Department of Electrical Engineering (ESAT), Faculty of Engineering, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium

    Marc Moonen

Authors
  1. Koen Eneman
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  2. Marc Moonen
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Correspondence to Koen Eneman.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Eneman, K., Moonen, M. Multimicrophone Speech Dereverberation: Experimental Validation. J AUDIO SPEECH MUSIC PROC. 2007, 051831 (2007). https://doi.org/10.1155/2007/51831

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Keywords

  • Acoustics
  • Performance Index
  • Classical Solution
  • Speech Recognition
  • Experimental Validation