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Eric Durant, P.E., Ph.D. - Milwaukee School of Engineering. Milwaukee, WI, US

Eric Durant, P.E., Ph.D. Eric Durant, P.E., Ph.D.

Professor, Program Director | Milwaukee School of Engineering

Milwaukee, WI, UNITED STATES

Dr. Eric Durant's research focuses on real-time audio processing with a focus on hearing aids and artificial intelligence/deep learning.

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51: PHD in Electrical Engineering and MSOE College Professor-Dr. Eric Durant

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Education, Licensure and Certification (6)

Professional Engineer: WI License 45011-6

Executive M.B.A.: Business, University of Wisconsin-Milwaukee 2011

Ph.D.: Electrical Engineering, University of Michigan 2002

M.S.E.: Electrical Engineering, University of Michigan 1999

B.S.: Computer Engineering, Milwaukee School of Engineering 1998

B.S.: Electrical Engineering, Milwaukee School of Engineering 1998

Biography

Dr. Eric Durant is a professor and computer engineering program director at MSOE in the Electrical Engineering and Computer Science Department. Durant's research includes using real-time audio processing with a focus on hearing aids and artificial intelligence/deep learning. He also has used genetic algorithms to efficiently fit audio processing parameters in hearing aids, robust perceptual rank inferencing, beamforming, convex optimization, deep learning, and spatialization. He is a senior DSP research engineer II for Starkey Hearing Technologies and was a visiting professor at NVIDIA.

Areas of Expertise (8)

Deep Learning

Audio Processing

Beamforming

Electrical Engineering

Computer Engineering

Genetic Algorithms

Convex Optimization

Hearing Aids

Accomplishments (6)

Order of the Engineer, MSOE inductee #500

2019

MSOE Alumni Achievement Award

2017

Oscar Werwath Distinguished Teacher Award, MSOE

2016

MSOE Inaugural Student Athletic Advisory Committee (SAAC) Faculty Recognition Award

Awarded for contributions in MSOE sports photography, May, 2015

STEM Forward Young Engineer of the Year

2013

EECS Department Fellowship

University of Michigan, 1998-2002

Affiliations (3)

  • American Society for Engineering Education (ASEE) : Member
  • Institute of Electrical and Electronics Engineers (IEEE) : Senior Member
  • ABET PEV : Computer and Electrical Engineering Program Evaluator

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Media Appearances (1)

Faculty and staff honored at MSOE

MSOE  

2016-05-09

Dr. Eric Durant ’98, professor and computer engineering program director, received the Oscar Werwath Distinguished Teacher Award. The award was established by the university in 1967 to recognize excellence in teaching. All nominees for this award must have a minimum of seven years of full-time service to MSOE. Students choose the award winner through two rounds of voting. On their ballots, students described Durant as a passionate teacher who clearly wishes for students’ success; extremely dedicated to being the best educator and mentor he can be; incredibly nice and humble; helpful to students, even those he hasn’t met before; by far the most caring professor I have ever had; and he always makes times to answer questions outside of class.

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Patents (7)

Method and apparatus for localization of streaming sources in hearing assistance system

US9930456B2

2018 A hearing assistance system streams audio signals from one or more streaming sources to a hearing aid set and enhances the audio signals such that the output sounds transmitted to the hearing aid wearer include a spatialization effect allowing for localization of each of the one more streaming sources. The system determines the position of the hearing aid set relative to each streaming source in real time and introduces the spatialization effect for that streaming source dynamically based on the determined position, such that the hearing aid wearer can experience a natural feeing of the acoustic environment.

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Method and apparatus for localization of streaming sources in hearing assistance system

US9584933B2

2017 A hearing assistance system streams audio signals from one or more streaming sources to a hearing aid set and enhances the audio signals such that the output sounds transmitted to the hearing aid wearer include a spatialization effect allowing for localization of each of the one more streaming sources. The system determines the position of the hearing aid set relative to each streaming source in real time and introduces the spatialization effect for that streaming source dynamically based on the determined position, such that the hearing aid wearer can experience a natural feeing of the acoustic environment.

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Method and apparatus for localization of streaming sources in hearing assistance system

US9124983B2

2015 A hearing assistance system streams audio signals from one or more streaming sources to a hearing aid set and enhances the audio signals such that the output sounds transmitted to the hearing aid wearer include a spatialization effect allowing for localization of each of the one more streaming sources. The system determines the position of the hearing aid set relative to each streaming source in real time and introduces the spatialization effect for that streaming source dynamically based on the determined position, such that the hearing aid wearer can experience a natural feeing of the acoustic environment.

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Hearing aids and methods and apparatus for audio fitting thereof

US9049529

2015 A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

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Genetic algorithms with robust rank estimation for hearing assistance devices

US8,359,283

2013 Disclosed herein, among other things, is a method for fitting a hearing assistance device for a user using a genetic algorithm. Stimulus pairs are presented to the user using a computer, the stimulus pairs adapted to provide contrasting options for selection from a set of stimuli stored in the computer. Inputs are received from the user entered into the computer, including preference judgments of the user. A score is calculated for each stimulus of the pair using the computer to execute a rank agreement function to maximize agreement between scores and the preference judgments. A set of genes is selected based on the scores, where the set of genes correspond to hearing assistance device parameters. The set of genes is operated on with a genetic algorithm using the assigned scores to obtain a child set of genes. The child set is used to provide parameter values during fitting.

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Efficient convex optimization for real-time robust beamforming with microphone arrays

US8824711

2011 Disclosed herein, among other things, are methods and apparatus for improving speech intelligibility for speech-in-noise in audio processing and hearing assistance devices. The present subject matter includes a method for improving speech intelligibility for speech-in-noise for audio processing and hearing assistance devices. The method includes receiving an audio signal using a microphone array and processing the received signal to improve speech intelligibility in noise. A barrier-type beamforming process is used to improve signal-to-noise ratio at the output of the microphone array. The beamforming process includes convex optimization using a logarithmic barrier function, according to various embodiments.

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Hearing aids and methods and apparatus for audio fitting thereof

US7,650,004

2010 A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

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Research Grants (1)

Computer and Software Engineering Curricula Development Workshops

NSF Grant 1338752 $10,000

2013 Co-PI with Mark Ardis of Stevens Institute of Technology

Selected Publications (7)

CE2016: Updated curricular guidelines for computer engineering

IEEE Frontiers in Education Conference (FIE)

Nelson, V., Durant, E., Impagliazzo, J., Hughes, J.L.

2017 The report, Curriculum Guidelines for Undergraduate Degree Programs in Computer Engineering (CE2016), developed by the Association for Computing Machinery and the IEEE Computer Society, was released in December of 2016. This is one volume of a series of reports covering curricula for a variety of computing fields; it is a significant update of the previous version, CE2004. This paper discusses significant aspects of CE2016, with a focus on how the report might be used in reviewing, updating, and creating computer engineering programs.

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Perceptually motivated ANC for hearing-impaired listeners

IEEE

Eric Durant, Jinjun Xiao, Buye Xu, Martin McKinney, Tao Zhang

2013 The goal of noise control in hearing aids is to improve listening perception. In this paper we propose modifying a perceptually motivated active noise control (ANC) algorithm by incorporating a perceptual model into the cost function, resulting in a dynamic residual noise spectrum shaping technique based on the time-varying residual noise. The perceptual criterion to be minimized could be sharpness, discordance, annoyance, etc. As an illustrative example, we use loudness perceived by a hearing-impaired listener as the cost function. Specifically, we design the spectrum shaping filter using the listener's hearing loss and the dynamic residual noise spectrum. Simulations show significant improvements of 3-4 sones over energy reduction (ER) for severe high-frequency losses for some common noises that would be 6-12 without processing. However, average loudness across a wide range of noises is only slightly better than with ER, with greater improvements realized with increasing hearing loss. We analyze one way in which the algorithm fails and trace it to over-reliance on the common psychoacoustic modelling simplification that auditory channels are independent to a first approximation. This suggests future work that may improve performance.

Efficient convex optimization for real-time robust beamforming with microphone arrays

IEEE

Eric Durant, Ivo Merks, Bill Woods, Jinjun Xiao, Tao Zhang, Zhi-Quan Luo

2011 This paper presents an efficient implementation of a robust adaptive beamforming algorithm based on convex optimization for applications in the processing-constrained environment of a digital hearing aid. Several modifications of the standard interior point barrier method are introduced for use where the array data covariance matrix is changing rapidly relative to the algorithm's convergence rate. These efficiency improvements significantly simplify the computation without affecting the algorithm's fast convergence, and are useful for real-time adaptive beamforming regardless of the rate of array correlation change. Simulation results show that this implementation is numerically stable and succeeds where many minimum-variance distortionless response (MVDR) solutions fail.

Rubrics for assessing oral communication in the capstone design experience: Development, application, analysis and refinement

The International Journal of Engineering Education

Henry Welch, Deepti Suri, Eric Durant

2009 The importance of good communication skills is becoming increasingly relevant to engineers in today's globally competitive environment. The Accreditation Board for Engineering and Technology (ABET), recognizing this phenomenon, introduced six professional skills along with the various hard skills in their new accreditation criteria EC2000 for all engineering programs. At the Milwaukee School of Engineering (MSOE), rubrics were developed to aid in assessing the oral presentations made during the capstone senior design sequence. These rubrics have been applied by various senior design professors each quarter to assess all the mid-quarter presentations. The analysis (using the Spearman Rank Correlation Test and a Rater Disagreement Metric) of data collected over four quarters indicates that by repeatedly applying, analyzing and refining a rubric, it is possible to minimize the often subjective means of evaluating communication skills and move towards more objective evaluations. Over the past three years, multiple evaluators have shown strong agreement in the quality of student presentations. However, they have not yet arrived at a complete consensus indicating that we as yet do not have a completely reliable and objective tool and more work needs to be done in this area.

Efficient perceptual tuning of hearing aids with genetic algorithms

IEEE

Eric A Durant, Gregory H Wakefield, Dianne J Van Tasell, Martin E Rickert

2004 We describe a system for integrating a genetic algorithm (GA) with perceptual feedback to perform an efficient search in a perceptual space. The main system components are an efficient method for estimating perceptual rank order and genetic operators that take advantage of the types of parameters found in certain classes of audio processing systems. Preference judgments are used, resulting in a lightweight user interface. The application to subjectively fitting a portable hearing aid based solely on binary feedback is discussed. An experiment was conducted using eight normal and eight hearing impaired subjects. Three parameters were varied to control cancellation of acoustic feedback. The GA worked well for fitting this system, as indicated by both objective and subjective measures. In addition, users had greatly differing preferences for feedback cancellation parameters and these preferences did not change much when subjects were retested.

Hearing aid fitting with genetic algorithms Authors Eric Alan Durant

semanticscholar.org

Eric Alan Durant

2002 Fitting modern DSP-based hearing aids, which have numerous parameters, is a challenge for even the experienced audiologist. Simple but proven fitting rationales are used, limiting the prescribed setting to a subset of the hearing aid’s capabilities [7]. Given the success of the genetic algorithm (GA) in tuning simpler systems with subjective feedback [55, 58, 66], we propose it as an efficient and robust method for fitting hearing aids. GAs borrow biological concepts such as natural selection and mutation and apply them to non-biological search and optimization problems. Many previous studies of genetic algorithms for perceptual tuning [54, 59] and other adaptive hearing aid fitting methods1 [46] were conducted in tightly controlled laboratory environments, limiting their applicability to real-world use. In contrast, our approach uses a simple paired comparison input method, suitable for both clinical and unsupervised field use. This input method provides an easy way to integrate patient input into the fitting procedure, which is recognized as a key to successful fitting by the hearing aid research community [10]. We first investigate whether a GA can guide a hearing-impaired subject towards perceptually good feedback canceller parameters in everyday conditions. Feedback occurs when hearing aid gain is increased so far that the portion of the signal at the microphone due to the hearing aid output is greater than the portion from the environment. Since the output is fed back and amplified further, the hearing aid quickly reaches its maximum output level. This results in a loud squealing sound.

Efficient model fitting using a genetic algorithm: pole-zero approximations of HRTFs

IEEE

Eric A Durant, Gregory H Wakefield

2002 We demonstrate that a genetic algorithm (GA) can efficiently generate accurate low-order pole-zero approximations of head-related transfer functions (HRTFs) from measured impulse responses by minimizing a logarithmic error criterion. This approach is much simpler and comparable or superior in efficiency to competing search algorithms. We build on previous work in low-order HRTF approximation. By applying the GA, we converge to solutions of equal quality in about 30 s compared to over 20 min. This work develops a basic steady-state GA using a pole-zero filter design problem as an illustrative example. We propose a domain-appropriate error measure. We then apply the algorithm to designing filters to approximate measured HRTFs. Detailed performance measurements are presented. In the appendix, we propose a widely applicable population variation metric. A lower bound is developed for this metric and is used to detect convergence.