- Volume 11 Issue 2
A Novel Approach for Blind Estimation of Reverberation Time using Gamma Distribution Model
DOI QR Code
Hamza, Amad;Jan, Tariqullah;Jehangir, Asiya;Shah, Waqar;Zafar, Haseeb;Asif, M.
- 투고 : 2014.04.09
- 심사 : 2015.11.03
- 발행 : 2016.03.01
In this paper we proposed an unsupervised algorithm to estimate the reverberation time (RT) directly from the reverberant speech signal. For estimation process we use maximum likelihood estimation (MLE) which is a very well-known and state of the art method for estimation in the field of signal processing. All existing RT estimation methods are based on the decay rate distribution. The decay rate can be obtained either from the energy envelop decay curve analysis of noise source when it is switch off or from decay curve of impulse response of an enclosure. The analysis of a pre-existing method of reverberation time estimation is the foundation of the proposed method. In one of the state of the art method, the reverberation decay is modeled as a Laplacian distribution. In this paper, the proposed method models the reverberation decay as a Gamma distribution along with the unification of an effective technique for spotting free decay in reverberant speech. Maximum likelihood estimation technique is then used to estimate the RT from the free decays. The method was motivated by our observation that the RT of a reverberant signal when falls in specific range, then the decay rate of the signal follows Gamma distribution. Experiments are carried out on different reverberant speech signal to measure the accuracy of the suggested method. The experimental results reveal that the proposed method performs better and the accuracy is high in comparison to the state of the art method.
Gamma distribution;Maximum likelihood estimation;Reverberant signal analysis;Reverberation time
- H. Kuttruff, Room Acoustics, Elsevier Science Publishers Ltd., Lindin, 3rd ed., 1991.
- R. Ratnam, D. L. Jones, B. C. Wheeler, W. D. OBrien, C. R. Lansing and A. S. Feng, “Blind estimation of reverberation time,” J. Acoust. Soc. Am., vol. 114, pp. 2877-2892, Nov. 2003. https://doi.org/10.1121/1.1616578
- W. C. Sabine, “Collected Papers on Acoustics,” 1922.
- International Organization for Standardization (ISO), Geneva, Acoustics- Measurements of the Reverberation Time of Rooms with Reference to Other Acoustical Parameters, 1997
- K. Lebart, J. Boucher, and P. Denbigh, “A new method based on spectral subtraction for speech deriverberation,” Acta Acustica, vol. 87, pp. 359-366, 2001.
- S. Vesa and A. Harma, “Automatic estimation of reverberation time from binaural signals,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., vol. 3, 2005, pp. 281-284.
- M. R. Schroeder, “New method of measuring reverberation time,” J. Acoust. Soc. Am., pp. 409-412, 1965.
- H. W. Lollmann, E. Yilmaz, M. Jeub and P. Vary, “An improved algorithm for blind reverberation time estimation,” Proc. Int. Workshop Acoust. Echo and Noise Control (IWAENC), Aug. 2010, Tel Aviv, Israel
- H. W. Lollmann and P. Vary, “Estimation of the Reverberation Time in Noisy Environments,” Proc. Int. Workshop Acoust. Echo and Noise Control (IWAENC), Sep. 2008, Washington USA.
- R. Ratnam, D. L. Jones and W. D. O Brien, “Fast algorithms for blind estimation of reverberation time,” IEEE Signal Process. Letters, vol. 11, pp. 537-540, Jun. 2004 https://doi.org/10.1109/LSP.2004.826667
- J.Y.C. Wen, E.A.P. Habets, and P.A. Naylor, “Blind estimation of reverberation time based on the distribution of signal decay rates,” Proc. IEEE Int. Conf. Acoust., Speech, and Signal Process., pp. 329-332, 2008.
- Scharrer, Roman, and M. Vorländer. “Blind reverberation time estimation.” Proceedings of the International Conference on Acoustics, Sydney, Australia. 2010.
- T. J. Cox, F. Li and P. Darlington, “Extracting room everberation time from speech using artificial neural networks,” FJ. Audio Engineering Soc., pp. 219-230, 2001.
- J. Nannariello and F. Fricke, “The prediction of reverberation time using neural network analysis,” Applied Acoust., vol. 58, pp. 305-325, 1999. https://doi.org/10.1016/S0003-682X(98)00081-4
- Y. Tahara and T. Miyajima, “A new approach to optimum reverberation time characteristics,” Applied Acoust., vol. 54, pp. 113-129, 1998. https://doi.org/10.1016/S0003-682X(97)00072-8
- Aliabadi, M., Golmohammadi, R., Ohadi, A., Mansoorizadeh, Z., Khotanlou, H., &Sarrafzadeh, M. S. (2014). Development of an Empirical Acoustic Model for Predicting Reverberation Time in Typical Industrial Workrooms Using Artificial Neural Networks. Acta Acustica united with Acustica, 100 vol. 6, pp.1090-1097. https://doi.org/10.3813/AAA.918788
- T. Petsatodis, C. Boukis, F. Talantzis, Z. Tan and R. Prasad, “Convex combination of multiple statistical models with application to VAD,” IEEE Trans. Audio, Speech, and Lang. Process., pp. 2314-2327, 2011.
- M. A. Bean (2001). Probability:The science of uncertainty with application to investments, insurance and engineering [Online]. Available:books.google.com.pk/books?isbn=0821847929
- V. Poor, “An Introduction to Signal Detection and Estimation,” Springer-Verlag, New York, 1994.
- M. Jeub, M. Schafer and P. Vary, “A binaural room impulse response database for the evaluation of dereverberation algorithms,” Proc. Int. Conf. Digital Signal Process. (DSP), 2009, Santorini, Greece.
- T. Jan, W. Wang, “Blind reverberation time estimation based on laplace distribution,” 20th EUSIPCO 2012