Yariv Ephraim

Yariv Ephraim received the B.Sc., M.Sc. and D.Sc. degrees in 1977, 1979 and 1984, respectively, all in Electrical Engineering, from the Technion-Israel Institute of Technology, Haifa, Israel. His doctoral dissertation focused on Enhancement of Noisy Speech Signals. From 1984-1985 he was a Rothschild Post-Doctoral Fellow at the Information Systems Laboratory, Stanford University, Palo Alto, CA.  At Stanford he worked on coding of noisy sources and on minimum discrimination information parametric modeling. In 1985 he joined the Information Principles Research Laboratory, AT&T Bell Labs, Murray Hill, NJ, where he served as a Member of Technical Staff until 1993. At Bell Labs his research focused on the theory of hidden Markov models and its application to speech enhancement and speech recognition. He joined George Mason University, Fairfax, VA, in 1991 and was promoted to full Professor of Electrical and Computer Engineering in 1999. His research work at Mason focused on signal and parameter estimation of partially observable  bivariate Markov processes in discrete and continuous time. Yariv Ephraim was elected Fellow of the IEEE in 1994 and he became IEEE Life-Fellow in 2016. He is a co-recipient of the 1999-2000  EURASIP Best Paper Award;  a co-recipient of the 2020 IEEE Signal Processing Society Sustained Impact Paper Award; and a co-recipient of the IEEE ICC-2021 Best Paper Award. He was Associate Editor of the IEEE Transactions on Audio, Speech and Language Processing from 2006-2009.  He served on the Editorial Board of Foundations and Trends in Signal Processing from its inception in 2007 until 2023. He retired from George Mason University in May 2024.

Email: yephraim@gmu.edu 

SELECTED JOURNAL PUBLICATIONS:

• H. Lev-Ari, Y. Ephraim and B.L. Mark, Traffic rate network tomography with higher-order cumulants. Networks, vol. 81, no. 2, pp. 220-234, Mar. 2023.
• N. Etemadi Rad, Y. Ephraim, and B.L. Mark, “Delay network tomography using a partially observable bivariate Markov chain,” IEEE/ACM Trans. on Networking, vol. 25, no. 1, pp. 126-138, Feb. 2017.
• Y. Ephraim and B.L. Mark, "Causal recursive parameter estimation for discrete-time hidden bivariate Markov chains," IEEE Trans. on Signal Processing, vol. 63, no. 8, pp. 2108-2117, April 2015.
• B.L. Mark and Y. Ephraim, "Explicit causal recursive estimators for continuous-time bivariate Markov chains," IEEE Trans. on Signal Processing, vol. 62, no. 10, pp. 2709-2718, May 2014.
• T. Nguyen, B.L. Mark, and Y. Ephraim, "Spectrum sensing using a hidden bivariate Markov model," IEEE Trans. on Wireless Communications, vol. 12, no. 9, pp. 4582-4591, Sept. 2013.
• Y. Ephraim and B. L. Mark, Bivariate Markov Processes and Their Estimation. Foundations and Trends in Signal Processing, Now Publishers, Boston, 2013. ISBN: 978-1601986740

• Y. Ephraim and B. L. Mark, “Consistency of maximum likelihood parameter estimation for bivariate Markov chains,” Stochastic Models, vol. 29, no. 1, pp. 89-111, 2013.
• B. L. Mark and Y. Ephraim, “An EM algorithm for continuous-time bivariate Markov chains,” Computational Statistics and Data Analysis, vol. 57, no. 1, pp. 504-517, Jan. 2013.
• Y. Ephraim and B. L. Mark, “Explicit forward recursive estimators for Markov modulated Markov processes,” Stochastic Models, vol. 28, no. 3, pp. 359-387, 2012.
• Y. Ephraim and M. A. Tinston, ``Iterative Schur Complement and Multistage Wiener Filtering,’’ SIAM J. Matrix Analysis and Applications, vol. 31, Issue 4, pp. 2222-2238, 2010.  
• W. J. J. Roberts and Y. Ephraim, ``An EM Algorithm for Ion-Channel Current Estimation,’’ IEEE Trans. Sig. Proc., IEEE Trans. Sig. Proc., vol. 56, pp. 26-33, Jan. 2008.  
• Y. Ephraim and I. Cohen, “Recent Advancements in Speech Enhancement,’’ The Electrical Engineering Handbook, CRC Press, 2006.
• Y. Ephraim and W. J. J. Roberts, “On Second-Order Statistics of Log-Periodogram with Correlated Components,’’ IEEE Sig. Proc. Let., vol. 12, pp. 625-628, Sep. 2005.
• Y. Ephraim and W. J. J. Roberts, “Revisiting Autoregressive Hidden Markov Modeling of Speech Signals,’’ IEEE Sig. Proc. Let., vol. 12,  pp. 166- 169, Feb. 2005.
• H. Lev-Ari and Y. Ephraim, “Extension of the signal subspace speech enhancement approach to colored noise,” IEEE Sig. Proc. Let., vol. 10, pp. 104-106, April 2003.
• Y. Ephraim and N. Merhav, “Hidden Markov processes,” IEEE Trans. Inform. Theory, vol. 48, pp. 1518-1569, June 2002.
• K. L. Bell, Y. Ephraim, and H. L. Van Trees, “A Bayesian approach to robust adaptive beamforming,” IEEE Trans. on Signal Processing, vol. 48, pp. 386-398, Feb. 2000.
• Y. Ephraim and M. Rahim, “On second order statistics and linear estimation of cepstral coefficients,” IEEE Trans. on Speech and Audio Processing, vol. 7, pp. 162-176, March 1999.
• K. Bell, Y. Steinberg, Y. Ephraim and H. L. Van Trees, “Extended Ziv-Zakai lower bound for vector parameter estimation,” IEEE Trans. Inform. Theory, vol. 43, pp. 624-637, March 1997. .
• Y. Ephraim and H. L. Van Trees, “A signal subspace approach for speech enhancement,” IEEE Trans. Speech and Audio Processing, vol. 3, pp. 251-266, July 1995.
• Y. Ephraim, “Statistical model-based speech enhancement systems,” IEEE Proc., vol. 80, pp. 1526-1555, Oct. 1992.  
• Y. Ephraim and N. Merhav, “Lower and upper bounds on the minimum mean square error in composite source signal estimation,” IEEE Trans. Inform. Theory, vol. IT-38, pp.1709-1724, Nov. 1992.
• Y. Ephraim, “Gain adapted hidden Markov models for recognition of clean and noisy speech,” IEEE Trans. Signal Processing, vol. SP-40, pp. 1303-1316, June 1992.
• Y. Ephraim, “A Bayesian estimation approach for speech enhancement using hidden Markov models,” IEEE Trans. Signal Processing, vol. SP-40, pp. 725-735, April 1992.
• N. Merhav and Y. Ephraim, “Hidden Markov modeling using a dominant state sequence with application to speech recognition,” Computer, Speech, and Language, vol. 5, no. 4, pp. 327-339, Oct. 1991.
• Y. Ephraim, D. Malah, and B.-H. Juang, “On the application of hidden Markov models for enhancing noisy speech,” IEEE Trans. Acoust. Speech, Signal Processing, vol. ASSP-37, pp. 1846-1856, Dec. 1989.
• Y. Ephraim, A. Dembo, and L. R. Rabiner, “A minimum discrimination information approach for hidden Markov modeling,” IEEE Trans. Inform. Theory, vol. IT-35, pp. 1001-1013, Sept. 1989.  
• Y. Ephraim, H. Lev-Ari, and R. M. Gray, “Asymptotic minimum discrimination information measure for asymptotically weakly stationary processes,” IEEE Trans. Inform. Theory, vol. IT-34, pp. 1033-1040, Sept. 1988.  
• Y. Ephraim and R. M. Gray, “A unified approach for encoding clean and noisy sources by means of waveform and autoregressive model vector quantization,” IEEE Trans. Inform. Theory, vol. IT-34, pp. 826-834, July 1988.
• Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error log-spectral amplitude estimator,” IEEE Trans. on Acoust., Speech, Signal Processing, vol. ASSP-33, pp. 443-445, Apr. 1985.  
• Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short-time spectral amplitude estimator,” IEEE Trans. on Acoust., Speech, Signal Processing, vol. ASSP-32, pp. 1109-1121, Dec. 1984.