Skip to content


  • Research Article
  • Open Access

Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations

EURASIP Journal on Advances in Signal Processing20072007:034013

  • Received: 16 November 2006
  • Accepted: 2 February 2007
  • Published:


Inverse filtering of room transfer functions (RTFs) is considered an attractive approach for speech dereverberation given that the time invariance assumption of the used RTFs holds. However, in a realistic environment, this assumption is not necessarily guaranteed, and the performance is degraded because the RTFs fluctuate over time and the inverse filter fails to remove the effect of the RTFs. The inverse filter may amplify a small fluctuation in the RTFs and may cause large distortions in the filter's output. Moreover, when interference noise is present at the microphones, the filter may also amplify the noise. This paper proposes a design strategy for the inverse filter that is less sensitive to such disturbances. We consider that reducing the filter energy is the key to making the filter less sensitive to the disturbances. Using this idea as a basis, we focus on the influence of three design parameters on the filter energy and the performance, namely, the regularization parameter, modeling delay, and filter length. By adjusting these three design parameters, we confirm that the performance can be improved in the presence of RTF fluctuations and interference noise.


  • Design Parameter
  • Quantum Information
  • Design Strategy
  • Regularization Parameter
  • Realistic Environment

Authors’ Affiliations

Media Information Laboratory, NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun Kyoto, 619-0237, Japan


  1. Miyoshi M, Kaneda Y: Inverse filtering of room acoustics. IEEE Transactions on Acoustics, Speech, and Signal Processing 1988,36(2):145-152. 10.1109/29.1509View ArticleGoogle Scholar
  2. Furuya K, Kaneda Y: Two-channel blind deconvolution of nonminimum phase FIR systems. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 1997,E80-A(5):804-808.Google Scholar
  3. Hikichi T, Delcroix M, Miyoshi M: Blind dereverberation based on estimates of signal transmission channels without precise information on channel order. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), March 2005, Philadelphia, Pa, USA 1: 1069–1072.Google Scholar
  4. Huang Y, Benesty J, Chen J: A blind channel identification-based two-stage approach to separation and dereverberation of speech signals in a reverberant environment. IEEE Transactions on Speech and Audio Processing 2005,13(5):882-895.View ArticleGoogle Scholar
  5. Mourjopoulos J: On the variation and invertibility of room impulse response functions. Journal of Sound and Vibration 1985,102(2):217-228. 10.1016/S0022-460X(85)80054-7View ArticleGoogle Scholar
  6. Hikichi T, Itakura F: Time variation of room acoustic transfer functions and its effects on a multi-microphone dereverberation approach. Proceedings of the Workshop on Microphone Arrays: Theory, Design and Application, October 1994, Piscataway, NJ, USAGoogle Scholar
  7. Omura M, Yada M, Saruwatari H, Kajita S, Takeda K, Itakura F: Compensating of room acoustic transfer functions affected by change of room temperature. Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '99), March 1999, Phoenix, Ariz, USA 2: 941–944.Google Scholar
  8. Radlović BD, Williamson RC, Kennedy RA: Equalization in an acoustic reverberant environment: robustness results. IEEE Transactions on Speech and Audio Processing 2000,8(3):311-319. 10.1109/89.841213View ArticleGoogle Scholar
  9. Talantzis F, Ward DB: Robustness of multichannel equalization in an acoustic reverberant environment. The Journal of the Acoustical Society of America 2003,114(2):833-841. 10.1121/1.1594189View ArticleGoogle Scholar
  10. Tokuno H, Kirkeby O, Nelson PA, Hamada H: Inverse filter of sound reproduction systems using regularization. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 1997,E80-A(5):809-820.Google Scholar
  11. Hansen PC: The truncated SVD as a method for regularization. BIT Numerical Mathematics 1987,27(4):534-553. 10.1007/BF01937276MathSciNetView ArticleGoogle Scholar
  12. Tatekura Y, Nagata Y, Saruwatari H, Shikano K: Adaptive algorithm of iterative inverse filter relaxation to acoustic fluctuation in sound reproduction system. Proceedings of the 18th International Congress on Acoustics (ICA '04), April 2004, Kyoto, Japan 4: 3163–3166.Google Scholar
  13. Tatekura Y, Urata S, Saruwatari H, Shikano K: On-line relaxation algorithm applicable to acoustic fluctuation for inverse filter in multichannel sound reproduction system. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 2005,E88-A(7):1747-1756. 10.1093/ietfec/e88-a.7.1747View ArticleGoogle Scholar
  14. Kirkeby O, Nelson PA, Hamada H, Orduna-Bustamante F: Fast deconvolution of multichannel systems using regularization. IEEE Transactions on Speech and Audio Processing 1998,6(2):189-194. 10.1109/89.661479View ArticleGoogle Scholar
  15. Harville DA: Matrix Algebra from a Statistician's Perspective. Springer, New York, NY, USA; 1997.View ArticleGoogle Scholar
  16. Elliott SJ, Boucher CC, Nelson PA: The behavior of a multiple channel active control system. IEEE Transactions on Signal Processing 1992,40(5):1041-1052. 10.1109/78.134467View ArticleGoogle Scholar
  17. Hilgers JW: On the equivalence of regularization and certain reproducing kernel Hilbert space approaches for solving first kind problems. SIAM Journal on Numerical Analysis 1976,13(2):172-184. 10.1137/0713018MathSciNetView ArticleGoogle Scholar
  18. Kaminuma A, Ise S, Shikano K: A method of designing inverse system multi-channel sound reproduction system using least-norm-solution. Proceedings of the International Symposium on Active Control of Sound and Vibration (Active '99), December 1999, Fort Lauderdale, Fla, USA 2: 863–874.Google Scholar
  19. 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. 10.1121/1.382599View ArticleGoogle Scholar
  20. Martin R: Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Transactions on Speech and Audio Processing 2001,9(5):504-512. 10.1109/89.928915View ArticleGoogle Scholar


© Takafumi Hikichi et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.