Speech Enhancement Proposed by a Microphone Array using EM & HMM Model
Abstract
Keywords
Full Text:
PDFReferences
H.Attias, L.Deng, A.Acero, J.C.Platt (2001). Anew method for speech denoising using probabilistic models for clean speech and for noise.Proc.Eurospeech2001W.-K. Chen, Linear Networks and Systems (Book style). Belmont, CA: Wadsworth, 1993, pp. 123–135.
H. Attias, C.E. Schreiner (1998). Blind source separation and deconvolution: the dynamic component analysis algorithm. Neural Computation 10, 1373-1424.
Ephraim, Y. (1992). Statistical model based speech enhancement systems. Proc. IEEE 80(10), 1526-1555.
B.J. Frey, L. Deng, A. Acero, T. Kristjansson (2001). An iterative variational method for removing multiple types of acoustic distortion for robust speech recognition. Proc. Eurospeech 2001.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
S. Griebel, M. Brandstein (2001). Microphone array speech dereverberation using coarse channel modeling. Proc. ICASSP 2001.
Rabiner, L.R. A tutorial on hidden Markov models and selected applications in speech recognition.Proc.IEEE 77,257–286 (1989)
Dempster, A.P., Laird, N.M., Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1, , pp. 1-38
Redner, R.A., Walker, H.F. (Apr., 1984). Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM Review, Vol. 26, No. 2., pp. 195-239
M. Askar and H. Derin, A recursive algorithm for the Bayes solution of the smoothing problem, "IEEE Trans. Automatic Control, vol. 26, pp. 558-561, 1981
J. Benesty, J. Chen, and Y. Huang, “Speech Enhancement ” Springer-Verlag, Berlin, Germany, 2005
Philipos C. Loizou ,”Speech Enhancement: Theory and Practice ”,CRC Press. June 7, 2007.
Y. Ephraim and D. Malah, Signal to noise ratio estimation for enhancing speech using the Viterbi algorithm," Technion, EE Pub. No. 489, Mar. 1984
Bilmes, Jeff A. (1998). A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models. Berkeley, CA: International Computer Science Institute. pp. 7–13.
R. Redner and H. Walker. Mixture densities, maximum likelihood and the em algorithm.SIAM Review, 26(2), 1984.
Breeding, Andy (2004). The Music Internet Untangled: Using Online Services to Expand Your Musical Horizons. Giant Path. p. 128. ISBN 9781932340020.
M. Jordan and L. Xu. Convergence results for the em approach to mixtures of experts architectures. Neural Networks 8:1409–1431, 1996.
M.I. Jordan, Z. Ghahramani, T .S. Jaakk ola,L.K. Saul(1999).An introduction to variational methods in graphical models. Mac hine Learning 37,183-233.
Cohen, On speech enhancement under signal presence uncertainty, Proceedings of the 26th IEEE International Conference on Acoustics Speech, and Signal Processing, ICASSP-01, Salt Lake City, Utah, 7–11 May 2001.
D. Malah, R.V. Cox, A.J. Accardi, Tracking speech-presence uncertainty to improve speech enhancement in non-stationarynoise environments, Proceedings of the 24th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP-99, Phoenix, Arizona, 15–19 March 1999, pp. 789–792.
Marin, J.M., Mengersen, K. and Robert, C.P. "Bayesian modelling and inference on mixtures of distributions". Handbook of Statistics 25, D. Dey and C.R. Rao (eds). Elsevier-Sciences.
M. S. Arulampalam, S. Maskell, N. Gordon and T. Clapp,A tutorial on particle Ølters for online nonlinear/non-Gaussian Bayesian tracking Signal Processing," IEEE Trans. Signal Proc., vol. 50, pp. 174 -188, Feb. 2002
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.