Michael T. Johnson, Ph.D., P.E.

Name

Professor Electrical and Computer Engineering
Haggerty Hall 214
(414) 288-0631
mike.johnson@marquette.edu
Website

 

 

Research Interests

  • Speech Processing: speech recognition and enhancement, natural language processing
  • Signal Processing: Bioacoustics, microphone array processing, signal enhancement
  • Pattern Recognition and Machine Learning: statistical estimation and classification

Professional Preparation

Ph.D., 2000, Electrical Engineering,Purdue University
M.S., 1994, Electrical Engineering, University of Texas at San Antonio
B.S., 1990, CSE, LeTourneau University
B.S., 1989, Electrical Engineering, LeTourneau University

Selected Recent Publications

Trawicki, M. B., Johnson, M. T., Beta-order minimum mean-square error multichannel spectral amplitude estimation for speech enhancement. Adaptive Control and Signal Processing.

Kershenbaum, A., Blumstein, D., Roch, M., Johnson, M. T., etal (2014). Acoustic sequences in non‐human animals: a tutorial review and prospectus. Biological Reviews.

Liu, W., Zhang, W., Johnson, M. T., Liu, J. (2014). Homogenous ensemble phonotactic language recognition based on SVM supervector reconstruction. EURASIP Journal on Audio, Speech, and Music Processing, 42 (1).

Yu, C., Wójcicki, K., P. L., J. H., Johnson, M. T. (2014). Evaluation of the importance of time-frequency contributions to speech intelligibility in noise. The Journal of the Acoustical Society of America, 135 (5), 3007-3016.

Trawicki, M. B., Johnson, M. T. (2014). Speech enhancement using Bayesian estimators of the perceptually-motivated short-time spectral amplitude (STSA) with Chi speech priors. Speech Communication, 57 (2), 101-103.

Zhao, J., Zhang, W.-Q., Yuan, H., Johnson, M. T., Liu, J., Xia, S. (2013). Exploiting contextual information for prosodic event detection using auto-context. EURASIP Journal on Audio, Speech, and Music Processing, 2013 (1), 1-14.

Trawicki, M. B., Johnson, M. T. (2013). Distributed multichannel speech enhancement based on perceptually-motivated Bayesian estimators of the spectral amplitude. IET Signal Processing, 7 (4), 337-244.

Ji, A., Johnson, M. T., Walsh, E., McGee, J., Armstrong, D. (2013). Discrimination of individual tigers (Panthera tigris) from long distance roars. The Journal of the Acoustical Society of America, 133 (4), 1762-1769.

Shi, Y., Johnson, M. T., Zhang, W., Liu, J. (2013). RNN language model with word clustering and class-based output layer. EURASIP Journal on Audio, Speech, and Music Processing, 2013 (1), 1-7.

Recent Grants

Johnson, Michael T (Principal), Berry, Jeffrey (Co-Principal), "Speaker Independent Acoustic-Articulator Inversion for Pronunciation Assessment," Sponsored by National Science Foundation, Federal. (August 2013 - July 2016).

Berry, Jeffrey (Principal), Johnson, Michael T (Co-Principal), "Speech Rehabilitation Using a Virtual Vocal Tract.," Sponsored by Marquette University Regular Research Grant, Marquette University. (January 2015 - December 2015).

Johnson, Michael T, Berry, Jeffrey, "REU Supplement: Acoustic Articulator inversion for Computer Aided Pronunciation Training," Sponsored by NSF, Federal. (October 2014 - August 2015).

 

Student studying on campus

Overview

Electrical engineers work on the generation, transmission and distribution of electricity and how we use it. Computer engineers design and build the hardware and software that run our global marketplace. Think about the gadgets you plug into on a daily basis. How many of them are powered by electricity or computer technology?

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