dr.ir. R.C. Hendriks

Associate Professor
Circuits and Systems (CAS), Department of Microelectronics

Expertise: Audio signal processing, signal processing for hearing aids, biomedical signal processing

Themes: Health and Wellbeing

Biography

Richard C. Hendriks was born in Schiedam, The Netherlands. He received the B.Sc., M.Sc. (cum laude) and Ph.D. (cum laude) degrees in electrical engineering from the Delft University of Technology, Delft, The Netherlands, in 2001, 2003 and 2008, respectively.

Currently, he is an assistant professor in the Circuits and Systems (signal processing) group of the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. In March 2010 he received a VENI grant for his proposal "Intelligibility Enhancement for Speech Communication Systems"

Research visits

  1. September 2005 till December 2005: Visiting Researcher at the Institute of Communication Acoustics, Ruhr-University Bochum, Bochum, Germany.
  2. March 2008 till March 2009: Visiting researcher at Oticon A/S, Smørum, Denmark.

Research interest

His main research interests are digital speech and audio processing, including single- and multi-microphone acoustical noise reduction, speech enhancement and intelligibility of speech in noise.

Available implementations from various projects

Microphone Subset Selection
  1. Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

    Related papers:

    • J. Zhang, S. P. Chepuri, R. C. Hendriks and R. Heusdens. Microphone Subset Selection for MVDR Beamformer Based Noise Reduction, IEEE/ACM Trans. Audio, Speech, Language Process., 2018.

    Code: SeSelMVDR.zip

Intelligibility prediction
  1. STOI – Short-Time Objective Intelligibility

    Related papers:

    • C. H. Taal, R. C. Hendriks, R. Heusdens and J. Jensen. A Short-Time Objective Intelligibility Measure for Time-Frequency Weighted Noisy Speech, IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp. 4214 - 4217, 2010.
    • C. H. Taal, R. C. Hendriks, R. Heusdens and J. Jensen. An Algorithm for Intelligibility Prediction of Time-Frequency Weighted Noisy Speech, IEEE Trans. Audio, Speech, Language Process., vol. 19, no. 7 pp. 2125-2136, 2011.

    Code: stoi.m

  2. Speech intelligibility in bits - SIIB

    Related papers:

    Code: siib_demo1.zip (updated on 23/8/17)

Intelligibility enhancement
  1. Joint near-end and far-end intelligibility enhancement based on mutual information

    Related papers:

    • S. Khademi, R. C. Hendriks and W. B. Kleijn. Intelligibility Enhancement Based on Mutual Information, IEEE/ACM Trans. Audio, Speech, Language Process., vol. 25, issue 8, pp. 1694 - 1708, Aug. 2017.
    • S. Khademi, R. C. Hendriks and W. B. Kleijn. Jointly optimal near-end and far-end multimicrophone speech intelligibility enhancement based on mutual information, In Proc. IEEE Int. Conf. Acoustics, Speech, Signal Proc. (ICASSP), 2016.

    Code: JOINT_INT_ENH_MI.zip

  2. Optimal energy redistribution for speech enhancement based on a simple model for communication.

    Related papers:

    • W.B. Kleijn and R.C. Hendriks. “A simple model of speech communication and its application to intelligibility enhancement”, IEEE Signal Processing Letters, 2015.
    • W.B. Kleijn, J.B. Crespo, R.C. Hendriks, P. Petkov, B. Sauert and P. Vary. "Optimizing Speech Intelligibility in a Noisy Environment: A unified view", IEEE Signal Processing Magazine, Volume 32, Issue 2, pp. 43-54, March 2015.

    Code: optmi.zip

  3. Near-End Speech Enhancement Based on a Perceptual Distortion Measure

    Related papers:

    • C. H. Taal, R. C. Hendriks and R. Heusdens. Speech Energy Redistribution for Intelligibility Improvement in Noise Based on a Perceptual Distortion Measure, Computer Speech & Language, 2013.
    • C. H. Taal, R. C. Hendriks and R. Heusdens. A Speech Preprocessing Strategy For Intelligibility Improvement In Noise Based On A Perceptual Distortion Measure, IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp. 4061-4064, 2012.

    Code: nearend.zip

  4. STOI-Optimal N-of-M Channel Selection for Cochlear Implants

    Related papers:

    • C. H. Taal, R. C. Hendriks and R. Heusdens. Matching Pursuit for Channel Selection in Cochlear Implants Based on an Intelligibility Metric, EURASIP Europ. Signal Process. Conf. (EUSIPCO) , 2012.

    Code: stoi_mp.zip

Speech enhancement and noise PSD estimation
  1. Algorithm for Noise reduction for speech enhancement

    This is an implementation of alg. 3 described in the book DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement-A Survey of the State of the Art, by Richard C. Hendriks, Timo Gerkmann and Jesper Jensen; Morgan and Claypool Publishers, 2013.

    Related publication:

    • Richard C. Hendriks, Timo Gerkmann and Jesper Jensen, 'DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement-A Survey of the State of the Art', Morgan and Claypool Publishers, 2013.

    Code: code_nr_alg3_book.zip

  2. GenGam

    Toolbox for MMSE estimators of DFT coefficients under the generalized Gamma density

    Related papers:

    • J.S. Erkelens, R.C. Hendriks, R. Heusdens, and J. Jensen, "Minimum mean-square error estimation of discrete Fourier coefficients with generalized gamma priors", IEEE Trans. on Audio, Speech and Language Proc., vol. 15, no. 6, pp. 1741 - 1752, August 2007.
    • J.S. Erkelens, R.C. Hendriks and R. Heusdens "On the Estimation of Complex Speech DFT Coefficients without Assuming Independent Real and Imaginary Parts", IEEE Signal Processing Letters, 2008. and
    • R.C. Hendriks, J.S. Erkelens and R. Heusdens "Comparison of complex-DFT estimators with and without the independence assumption of real and imaginary parts", ICASSP, 2008.
    • R.C.Hendriks, R.Heusdens and J.Jensen "Log-spectral magnitude MMSE estimators under super-Gaussian densities", Interspeech, 2009.

    Code:

  3. Unbiased MMSE-based Noise Power Estimator

    Related papers:

    • Timo Gerkmann and Richard C. Hendriks, 'Unbiased MMSE-based Noise Power Estimation with Low Complexity and Low Tracking Delay', IEEE Trans. Audio, Speech and Language Processing, 2012.
    • Timo Gerkmann and Richard C. Hendriks, 'Noise Power Estimation Based on the Probability of Speech Presence', IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA, Oct. 2011.

    Code: noisepowproposed.m

  4. MMSE-based noise PSD tracker

    Related papers:

    • R. C. Hendriks, R. Heusdens and J. Jensen MMSE Based Noise PSD Tracking With Low Complexity, IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), pp. 4266-4269, 2010.

    Code: noise_tracker_V2.zip

General Signal Processing
  1. generalized discrete Fourier transform (gDFT)

    Related papers:

    • J. Martinez Castaneda, R. Heusdens and R. C. Hendriks. A Generalized Poisson Summation Formula and its Application to Fast Linear Convolution, IEEE Signal Process. Lett. , vol. 18, no. 9, pp. 501 -504, 2011.

    Code: GDFT_functions.zip

Current and past projects

  1. 2003 - 2008: STW project DET.6042 "Single-Microphone Enhancement of Noisy Speech Signals". A collaboration between Delft University of Technology and Philips Research.
  2. 2008 - 2013: STW project DIT.08051 "Intelligibility Enhancement of Noisy Speech". A collaboration between Delft University of Technology and Oticon A/S.
  3. July 2010 - present: Veni/STW project "Intelligibility Enhancement for Speech Communication Systems". A collaboration between Delft University of Technology and Bosch Security Systems B.V.
  4. 2010 - 2015: CSC project speech enhancement in wireless sensor networks.
  5. April 2014 - present: STW project Spatially Correct Multi-Microphone Noise
  6. 2016 - present: Smart sensing for Aviation.
  7. 2015 - present: CSC project speech enhancement in wireless sensor networks.
  8. 2015 - present: STW/Heart foundation project Earlier recognition of cardiovascular diseases (AFFIP)
  9. 2017 - present: CSC project Signal processing for atrial fibrillation.

EE2S31 Signal processing

Digital signal processing; stochastic processes

ET4386 Estimation and detection

Basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.

IN4182 Digital audio and speech processing

Audio, speech and acoustic signal processing, speech enhancement, microphone-array signal processing

Earlier recognition of cardiovascular diseases

Atrial Fibrillation FIngerPrinting: Spotting Bio-Electrical Markers to Early Recognize Atrial Fibrillation by the Use of a Bottom-Up Approach

Signal processing over wireless acoustic sensor networks

Microphone subset selection for WASNs

Smart sensing for Aviation

Detecting damages in composite material manufacturing

Spatially Correct Multi-Microphone Noise Reduction Strategies suitable for Hearing Aids

multichannel signal processing algorithms to help hearing aid users

Intelligibility enhancement for speech communication systems

Can we do "precoding" of speech signals to enhance their intelligibility at the receiver, taking channel distortions and environmental noise into account?

Projects history

Speech enhancement in wireless acoustic sensor networks

Distributed speech enhancement algorithms using a large number of microphones distributed in the environment

Intelligibility Enhancement of Noisy Speech

The objective of the project is to develop a speech enhancement system which specifically aims at improving the intelligibility of the speech signal.

General signal processing

Calculation of the Mean Strain of Smooth Non-uniform Strain Fields Using Conventional FBG Sensors

Matlab code for Calculation of the Mean Strain of Smooth Non-uniform Strain Fields Using Conventional FBG Sensors

Software, May 2018

Generalized discrete Fourier transform (gDFT)

A generalized Fourier transform is introduced that enables linear convolutions without the need of zero-padding. This results in faster, more resource- efficient computations

Software, Jan 2011

Multi-Microphone

Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

Software, Jan 2018

Speech enhancement and noise PSD estimation

Algorithm for Noise reduction for speech enhancement

DFT-based single channel noise reduction

Software, Jan 2013

Unbiased MMSE-based Noise Power Estimator

Software, Oct 2011

MMSE-based noise PSD tracker

Software, Mar 2010

GenGam Log MMSE

Toolbox for log-spectral magnitude MMSE estimators of DFT coefficients under super-Gaussian densities

Software, Jan 2009

GenGam

Toolbox for MMSE estimators of DFT coefficients under the generalized Gamma density

Software, Jan 2008

Speech intelligibility enhancement

Joint near-end and far-end intelligibility enhancement

Software, Jan 2017

Optimal energy redistribution for speech enhancement

Software, Feb 2015

STOI-Optimal N-of-M Channel Selection for Cochlear Implants

Software, Aug 2012

Near-End Speech Enhancement

Pre-processing algorithm to improve speech intelligibility in noise for the near-end listener.

Software, May 2012

Speech intelligibility prediction

STOI – Short-Time Objective Intelligibility

Software, Jan 2018

SIIB - Speech intelligibility in bits

Software, Jan 2018

Sensor Selection

Microphone Subset Selection for MVDR Beamformer Based Noise Reduction

Software, Jan 2018

Last updated: 22 Feb 2018