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Noise reduction in hearing aids: Generalised Sidelobe Canceller

Noise reduction in hearing aids: Generalised Sidelobe Canceller. Nico De Clercq Pieter Gijsenbergh. Overview. Problem & goals Implementation Spatial filtering Noise reduction (GSC) FDAF – LMS Performance measurements Results. Problem & goals. Problem:

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Noise reduction in hearing aids: Generalised Sidelobe Canceller

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

  2. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Results

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

  4. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Results

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

  6. Step 1: Spatial Filtering (2) • 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

  7. 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

  8. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Demo

  9. 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)

  10. Voice Activity Detection • 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

  11. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Results

  12. Algorithm: FDAF-LMS (1) • 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

  13. Algorithm: FDAF-LMS (2) • 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:

  14. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Results

  15. Performance measures • 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

  16. Overview • Problem & goals • Implementation • Spatial filtering • Noise reduction (GSC) • FDAF – LMS • Performance measurements • Results

  17. Step 1: Calibrating the filter

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

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

  20. Step 3: Noise reduction (GSC)

  21. Demo: Overlap-add/-save vs. Circular

  22. Demo: VAD vs. Perfect VAD • VAD introduces some extra distortion • Sensitive to the reference level

  23. Conclusion • Pretty good results • In practice • GSC performs not as good • Reflections are present • Limitations: speaker’s direction has to be known

  24. Reference • 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 • http://www.rp-photonics.com/interference.html

  25. Questions ?

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