Abstract
In this contribution, MAP spectral amplitude estimators for speech enhancement are presented. For single-microphone applications, efficient MAP estimators with a super-Gaussian speech model, that can be adapted with high accuracy towards the real distribution in a given system, are introduced. For multi-microphone applications, joint MAP estimators that also exploit spatial properties of speech and noise are derived. Both the integration of the more accurate speech model as well as the multi-microphone joint spectral amplitude estimation improve the performance of a common DFT domain speech enhancement system.
This work was carried out while being with the Institute of Communication Systems and Data Processing (IND) at the RWTH Aachen University, Germany
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
S. F. Boll, “Suppression of Acoustic Noise in Speech Using Spectral Subtraction,” IEEE Trans. Acoust., Speech and Signal Processing, vol. 27, pp. 113–120, 1979.
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator,” IEEE Trans. Acoust., Speech and Signal Processing, vol. 32, pp. 1109–1121, 1984.
R. Martin, “Speech enhancement using MMSE short time spectral estimation with gamma distributed priors,” in Proc. IEEE ICASSP, 2002, pp. 253–256.
P. J. Wolfe and S. J. Godsill, “Eficient Alternatives to the Ephraim-Malah Suppression Rule for Audio Signal Enhancement,” EURASIP Journal on Applied Signal Processing, Special Issue: Digital Audio for Multimedia Communications, vol. 2003, no. 11, pp. 1043–1051, 2003.
R. Martin, “Noise power spectral density estimation based on optimal smoothing and minimum statistics,” IEEE Trans. Speech and Audio Processing, vol. 9, pp. 504–512, 2001.
I. Cohen, “On the decision-directed estimation approach of Ephraim and Malah,” in Proc. IEEE ICASSP, 2004.
R. J. McAulay and M. L. Malpass, “Speech enhancement using a soft-decision noise suppression filter,” IEEE Trans. Acoust., Speech and Signal Processing, pp. 137–145, Apr. 1980.
D. R. Brillinger, Time Series, Data Analysis and Theory. McGraw-Hill, 1981.
H. Brehm and W. Stammler, “Description and Generation of Spherically Invariant Speech-Model Signals,” Elsevier Signal Processing, vol. 12, pp. 119–141, 1987.
R. Martin and C. Breithaupt, “Speech Enhancement in the DFT Domain using Laplacian Speech Priors,” in Proc. IWAENC, 2003, pp. 87–90.
T. Lotter, Single and Multichannel Speech Enhancement for Hearing Aids. Ph.D. Dissertation, Aachener Beiträge zu Digitalen Nachrichtensystemen (ed. P. Vary), RWTH Aachen University, 2004.
P. Vary, “Noise suppression by spectral magnitude estimation — Mechanisms and theoretical limits,” Signal Processing, vol. 8, pp. 387–400, 1985.
D. L. Wang and J. S. Jim, “The unimportance of phase in speech enhancement,” IEEE Trans. Acoust., Speech and Signal Processing, vol. ASSP-30, pp. 679–681, 1982.
I. S. Gradshteyn and I. M. Ryzhik, Table of Integrals, Series, and Products. Academic Press, Inc., 1994.
S. Kullback, Information Theory and Statistics. Dover Publication, 1968.
J. L. Melsa and D. L. Cohn, Decision and Estimation Theory. McGraw-Hill, 1978.
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech and Signal Processing, vol. 33, pp. 443–445, 1985.
T. Lotter, C. Benien, and P. Vary, “Multichannel direction-independent speech enhancement using spectral amplitude Estimation,” EURASIP Journal on Applied Signal Processing, Special Issue: Signal Processing for Acoustic Communication Systems, vol. 2003, no. 11, pp. 1147–1157, 2003.
G. W. Elko, “Spatial coherence functions for differential microphone in isotropic noise fields,” in Microphone Arrays, edited by M. Brandstein and D. B. Ward, Springer-Verlag, pp. 61–86, 2001.
D. Malah, R. V. Cox, and A. J. Accardi, “Tracking speech presence uncertainty to improve speech enhancement in non-stationary noise environments,” in Proc. IEEE ICASSP, 1999.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Lotter, T. (2005). Single- and Multi-Microphone Spectral Amplitude Estimation Using a Super-Gaussian Speech Model. In: Speech Enhancement. Signals and Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27489-8_4
Download citation
DOI: https://doi.org/10.1007/3-540-27489-8_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24039-6
Online ISBN: 978-3-540-27489-6
eBook Packages: EngineeringEngineering (R0)