dr.ir. R.C. Hendriks
Circuits and Systems (CAS), Department of Microelectronics
Expertise: Audio signal processingThemes: Audio and acoustic signal processing, Biomedical signal processing/wavefield imaging
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"
- September 2005 till December 2005: Visiting Researcher at the Institute of Communication Acoustics, Ruhr-University Bochum, Bochum, Germany.
- March 2008 till March 2009: Visiting researcher at Oticon A/S, Smørum, Denmark.
Research interestHis 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 projectsIntelligibility prediction
- STOI – Short-Time Objective Intelligibility
- 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.
- Speech intelligibility in bits - SIIB
S. Van Kuyk, W. B. Kleijn, and R. C. Hendriks, ‘An instrumental intelligibility metric based on information theory’, IEEE Signal Processing Letters, 2018. S. Van Kuyk, W. B. Kleijn, and R. C. Hendriks, ‘An evaluation of intrusive instrumental intelligibility metrics’, under review, 2017.
siib_demo1.zip (updated on 23/8/17)Intelligibility enhancement
- Joint near-end and far-end intelligibility enhancement based on mutual information
- 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.
- Optimal energy redistribution for speech enhancement based on a simple model for communication.
- 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.
- Near-End Speech Enhancement Based on a Perceptual Distortion Measure
- 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.
- STOI-Optimal N-of-M Channel Selection for Cochlear Implants
- 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.
stoi_mp.zipSpeech enhancement and noise PSD estimation
- 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.
- 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.
Toolbox for MMSE estimators of DFT coefficients under the generalized Gamma density
- 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.
- Unbiased MMSE-based Noise Power Estimator
- 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.
- MMSE-based noise PSD tracker
- 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.
- generalized discrete Fourier transform (gDFT)
- 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.
Current and past projects
- 2003 - 2008: STW project DET.6042 "Single-Microphone Enhancement of Noisy Speech Signals". A collaboration between Delft University of Technology and Philips Research.
- 2008 - 2013: STW project DIT.08051 "Intelligibility Enhancement of Noisy Speech". A collaboration between Delft University of Technology and Oticon A/S.
- 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.
- 2010 - 2015: CSC project speech enhancement in wireless sensor networks.
- April 2014 - present: STW project Spatially Correct Multi-Microphone Noise
- 2016 - present: Smart sensing for Aviation (crack) Reduction Strategies suitable for Hearing Aids.
- 2015 - present: CSC project speech enhancement in wireless sensor networks.
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?
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.
Last updated: 20 Nov 2017
MSc project proposals
- Cardiac Arrhythmia Data Analysis (several projects)
- Speech recognition and speech based validation systems for web subscriptions
- Speech intelligibility enhancement for public address systems
- Privacy preserving distributed speech enhancement in wireless sensor networks
- Beamforming for speech enhancement preserving spatial cues
- Noise PSD Estimation in reverberant environments for speech intelligibility enhancement algorithms
- Enhencement of Musical Perception
- Packet Loss Concealment algorithm for real-time wireless audio systems
BSc project proposals