2. Nico De Clercq Pieter Gijsenbergh Noise reduction in hearing aids: Generalised Sidelobe Canceller

3.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Results Overview

4.  Problem:  Speech + noise = reduced intelligibility  Goals:  Filter signal to remove noise  Limit distortion of speech  In practice also: limit delays  Our implementation: maximize performance Problem & goals

5.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Results Overview

6.  Beamforming with two microphones  Normally: fixed delay filters  We: LMS-based implementation:  48 tap FIR-filter Step 1: Spatial Filtering (1)

7.  Requires calibration stage:  Best: white noise coming from speaker’s direction  In theory: calibration on speech also possible ▪ Reduces GSC performance  Introduces a delay due to causality:  Delay length = half the adaptive filter length

8.  One of the noisy speech signals through the calibrated spatial filter  Constructive & destructive interference  2-Channel case => Blocking matrix = +/-:  Desired + output = speech reference  Desired – output = noise reference Step 2: Create reference signals

9.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Demo

10.  LMS adaptive filter:  Speech reference = desired  Noise reference = input  Useful signal = error  128-tap FIR-filter  Introduces another delay (=half the filter length)  Adapt only during non-speech activity Step 3: Noise Reduction (GSC)

11.  Calculate power in a reference frame:  Typical frame length: 30 ms  Compare the power to a reference value  Higher level: more speech detected as noise  Lower level: even noise might be undetected  Construct an adapt-vector

12.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Results

13.  General flow:  FFT(x)*W = Y  Real(IFFT(Y)) = y  Desired – y = e  E = FFT(e)  Inputs/outputs depend on method used:  Overlap-save/add: inputs overlap, only part of output is maintained  Circular convolution: no overlap, everything is considered useful

14.  Adaptation of W is possible  Initial weights are zero  Mu updated for faster convergence:  mu = 0.1  lamdba = 0.9  alpha = 0.1  Power in previous frame: 

15.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Results

16.  Signal-to-noise ratio: Should improve  Pass clean speech and noise trough system and compare the outputs  Only during speech activity  Apply weighting: ▪ not every frequency has the same importance  Speech distortion: Should be limited  Compare input speech with processed speech Performance measures

17.  Problem & goals  Implementation  Spatial filtering  Noise reduction (GSC)  FDAF – LMS  Performance measurements  Results

18. Step 1: Calibrating the filter

19. Step 2: Creating references 10 dB case 0 dB case

20. Step 3: Noise reduction (GSC) 0 dB case 10 dB case

21. Step 3: Noise reduction (GSC)

22. Demo: Overlap-add/-save vs. Circular Overlap-saveOverlap-addCircular- convolution 10 dB SNR_in : 3,23 dB SNR : 20,12 dB SD : 1,796 SNR : 20,17 dB SD : 1,7396 SNR : 0,5342 dB SD : 1,1691 5 dB SNR_in : -1,77 dB SNR : 20,32 dB SD : 1,796 SNR : 20,37 dB SD : 1,7967 SNR : 0,5352 dB SD : 1,1691 0 dB SNR_in : -6,71 dB SNR : 20,35 dB SD : 1,8733 SNR : 20,53 dB SD : 1,8044 SNR : 0,5307 dB SD : 1,2546

23. Demo: VAD vs. Perfect VAD  VAD introduces some extra distortion  Sensitive to the reference level Perfect VADVAD: Pref = 120 VAD: Pref = 95 Overlap – save: 10 dB case SNR : 20,12 dB SD : 1,796 SNR : 19,87 dB SD : 1,8039 VAD Results SNR : 18,09 dB SD : 1,7975 VAD Results

24.  Pretty good results  In practice  GSC performs not as good  Reflections are present  Limitations: speaker’s direction has to be known

25.  Suppression of acoustic noise in speech using spectral subtraction, S. Boll, IEEE ASSP, vol 27, no 2, 1979  H. Levitt, "Noise reduction in hearing aids: An overview", Journal of Rehabilitation Research and Development, vol. 38, no. 1, Jan./Feb. 2001, pp. 111-121.  J.J Shynk, "Frequency-domain and multirate adaptive filtering " Signal Processing Magazine, IEEE, Volume 9, Issue 1, Jan 1992 Page(s):14 - 37.  I. A. McCowan, “Robust Speech Recognition using Microphone Arrays”, PhD Thesis, Queensland University of Technology, Australia, 2001.  G. O. Glentis, “Implementation of Adaptive Generalized Sidelobe Cancellers using efficient complex valuedarithmetic”, International Journal of Applied Mathemethics and Computer Science, vol. 13, no. 4, 2003, p. 549-566  Marc Moonen and Ian Proudler, “An Introduction to Adaptive Signal Processing”,  https://gilbert.med.kuleuven.be/~koen/demo_beam/demo_beam.html https://gilbert.med.kuleuven.be/~koen/demo_beam/demo_beam.html  http://www.rp-photonics.com/interference.html http://www.rp-photonics.com/interference.html Reference

26. ?

27.  With VAD: Pref = 120  With VAD: Pref = 95