noise reduction in hearing aids: generalised sidelobe canceller
DESCRIPTION
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: - PowerPoint PPT PresentationTRANSCRIPT
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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