noise reduction in hearing aids: generalised sidelobe canceller

Nico De Clercq Pieter Gijsenbergh

Noise reduction in hearing aids: Generalised Sidelobe Canceller

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsResults

Overview

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

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsResults

Overview

Beamforming with two microphones

Normally: fixed delay filters

We: LMS-based implementation: 48 tap FIR-filter

Step 1: Spatial Filtering (1)

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

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

Overview

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsDemo

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)

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

Overview

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsResults

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

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:

2

1 10 2 1

1 , 0,..., 2 1

( ),..., ( )

m m m

N

P k P k X k m N

k diag P k P k

1mP k

1 ( ) 2k k k k k 1 HW W F g F μ X E

Overview

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsResults

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

Overview

Problem & goalsImplementation

Spatial filtering Noise reduction (GSC) FDAF – LMS

Performance measurementsResults

Step 1: Calibrating the filter

Step 2: Creating references

10 dB case

0 dB case

Step 3: Noise reduction (GSC)

0 dB case

10 dB case

Step 3: Noise reduction (GSC)

Demo: Overlap-add/-save vs. Circular

Overlap-save

Overlap-add

Circular-convolutio

n

10 dBSNR_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 dBSNR_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 dBSNR_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

Demo: VAD vs. Perfect VAD

VAD introduces some extra distortion Sensitive to the reference level

Perfect VAD

VAD: 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

Conclusion

Pretty good resultsIn practice

GSC performs not as good Reflections are present

Limitations: speaker’s direction has to be known

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

Reference

Questions

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