- Research Article
- Open Access
Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 034013 (2007)
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.
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
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.
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.
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.
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)80054-7
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, USA
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.
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. 10.1109/89.841213
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. 10.1121/1.1594189
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.
Hansen PC: The truncated SVD as a method for regularization. BIT Numerical Mathematics 1987,27(4):534-553. 10.1007/BF01937276
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.
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.7.1747
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. 10.1109/89.661479
Harville DA: Matrix Algebra from a Statistician's Perspective. Springer, New York, NY, USA; 1997.
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. 10.1109/78.134467
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. 10.1137/0713018
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.
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
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. 10.1109/89.928915
About this article
Cite this article
Hikichi, T., Delcroix, M. & Miyoshi, M. Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations. EURASIP J. Adv. Signal Process. 2007, 034013 (2007) doi:10.1155/2007/34013
- Design Parameter
- Quantum Information
- Design Strategy
- Regularization Parameter
- Realistic Environment