Design and low-cost implementation of a robust
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Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2 PowerPoint PPT Presentation


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Design and low-cost implementation of a robust multichannel noise reduction scheme for cochlear implants. Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2 1 Dept. of Electrical Engineering (ESAT-SCD), KU Leuven, Belgium 2 Laboratory for Exp. ORL, KU Leuven, Belgium

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Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2

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Simon doclo 1 ann spriet 1 2 marc moonen 1 jan wouters 2

Design and low-cost implementation of a robust multichannel noise reduction scheme for cochlear implants

Simon Doclo1, Ann Spriet1,2, Marc Moonen1, Jan Wouters2

1Dept. of Electrical Engineering (ESAT-SCD), KU Leuven, Belgium

2Laboratory for Exp. ORL, KU Leuven, Belgium

SPS-2004, 15.04.2004


Overview

Overview

  • Problem statement: hearing in background noise

  • Adaptive beamforming: GSC

    • not robust against model errors

  • Design of robust noise reduction algorithm

    • robust fixed spatial pre-processor

    • robust adaptive stage

  • Experimental results

  • Low-cost implementation of adaptive stage

    • stochastic gradient algorithms

    • computational complexity + memory

  • Conclusions


Problem statement

hearing aids and cochlear implants

Problem statement

  • Hearing problems effect more than 10% of population

  • Digital hearing instruments allow for advanced signal processing, resulting in improved speech understanding

  • Major problem: (directional) hearing in background noise

    • reduction of noise wrt useful speech signal

    • multiple microphones + DSP

    • current systems: simple fixed and adaptive beamforming

    • robustness important due to small inter-microphone distance

  • Introduction

    -Problem statement

    -State-of-the-art

    -GSC

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions

design of robust multi-microphone noise reduction scheme


State of the art noise reduction

Sensitive to a-priori assumptions

State-of-the-art noise reduction

  • Single-microphone techniques:

    • spectral subtraction, Kalman filter, subspace-based

    • only temporal and spectral information  limited performance

  • Multi-microphone techniques:

    • exploit spatial information

    • Fixed beamforming: fixed directivity pattern

    • Adaptive beamforming (e.g. GSC): adapt to differentacoustic environments  improved performance

    • Multi-channel Wiener filtering (MWF): MMSE estimate of speech component in microphones  improved robustness

  • Introduction

    -Problem statement

    -State-of-the-art

    -GSC

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions

Robust scheme, encompassing both GSC and MWF


Adaptive beamforming gsc

Adaptive Noise Canceller

Spatial pre-processing

(adaptation during noise)

Speechreference

Fixed beamformer

S

A(z)

Blocking matrix

B(z)

Noise references

Adaptive beamforming: GSC

  • Fixed spatial pre-processor:

    • Fixed beamformer creates speech reference

    • Blocking matrix creates noise references

  • Adaptive noise canceller:

    • Standard GSC minimises output noise power

  • Introduction

    -Problem statement

    -State-of-the-art

    -GSC

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions


Robustness against model errors

Limit distortion both in and

Robustness against model errors

  • Spatial pre-processor and adaptive stage rely on assumptions (e.g. no microphone mismatch, no reverberation,…)

  • In practice, these assumptions are often not satisfied

    • Distortion of speech component in speech reference

    • Leakage of speech into noise references, i.e.

  • Design of robust noise reduction algorithm:

    • Design of robust spatial pre-processor (fixed beamformer), e.g. using statistical knowledge about microphone characteristics

    • Design of robust adaptive stage, e.g. by taking speech distortion explicitly into account in optimisation criterion

  • Introduction

    -Problem statement

    -State-of-the-art

    -GSC

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions

Speech component in output signal gets distorted


Robust spatial pre processor

Measurement or calibration procedure

Incorporate specific (random) deviations in design

Robust spatial pre-processor

  • Small deviations from assumed microphone characteristics (gain, phase, position)  large deviations from desired directivity pattern, especially for small-size microphone arrays

  • In practice, microphone characteristics are never exactly known

  • Consider all feasible microphone characteristics and optimise

    • average performance using probability as weight

      • requires statistical knowledge about probability density functions

      • cost function: least-squares, eigenfilter, non-linear

    • worst-case performance  minimax optimisation problem

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions


Simulations

Simulations

  • N=3, positions: [-0.01 0 0.015] m, L=20, fs=8 kHz

  • Passband = 0o-60o, 300-4000 Hz (endfire)Stopband = 80o-180o, 300-4000 Hz

  • Robust design - average performance:Uniform pdf = gain (0.85-1.15) and phase (-5o-10o)

  • Deviation = [0.9 1.1 1.05] and [5o -2o 5o]

  • Non-linear design procedure (only amplitude, no phase)

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions


Simulations1

dB

dB

dB

dB

Angle (deg)

Angle (deg)

Angle (deg)

Angle (deg)

Frequency (Hz)

Frequency (Hz)

Frequency (Hz)

Frequency (Hz)

Simulations

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions


Design of robust adaptive stage

noise reduction

speech distortion

Design of robust adaptive stage

  • Distorted speech in output signal:

  • Robustness: limit by controlling adaptive filter

    • Quadratic inequality constraint (QIC-GSC):

      = conservative approach, constraint  f(amount of leakage)

    • Take speech distortion into account in optimisation criterion(SDW-MWF)

      • 1/ trades off noise reduction and speech distortion

      • Regularisation term ~ amount of speech leakage

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions

Limit speech distortion, while not affecting noise reduction performance in case of no model errors QIC


Wiener solution

speech-and-noise periods

noise-only periods

Wiener solution

  • Optimisation criterion:

  • Problem: clean speech and hence speech correlation matrix are unknown!

    Approximation:

  • VAD (voice activity detection) mechanism required!

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions


Spatially preprocessed sdw mwf

Multi-channel Wiener Filter (SDW-MWF)

Spatial preprocessing

Speechreference

Fixed beamformer

S

A(z)

Blocking matrix

B(z)

Noise references

Spatially-preprocessed SDW-MWF

  • SP-SDW-MWF encompasses both GSC and SDW-MWF:

    • No filter   speech distortion regularised GSC (SDR-GSC)

      • regularisation term added to GSC: the larger the speech leakage, the larger the regularisation

      • special case: 1/ = 0corresponds to traditional GSC

    • Filter  SDW-MWF on pre-processed microphone signals

      • in absence of model errors = cascade of GSC + single-channel postfilter

      • Model errors do not effect its performance!

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions


Experimental validation 1

Experimental validation (1)

  • Set-up:

    • 3-mic BTE on dummy head (d = 1cm, 1.5cm)

    • Speech source in front of dummy head (0)

    • 5 speech-like noise sources: 75,120,180,240,285

    • Microphone gain mismatch at 2nd microphone

  • Performance measures:

    • Intelligibility-weighted signal-to-noise ratio

      • Ii = band importance of i th one-third octave band

      • SNRi= signal-to-noise ratio in i th one-third octave band

    • Intelligibility-weighted spectral distortion

      • SDi= average spectral distortion in i th one-third octave band

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions

(Power Transfer Function for speech component)


Experimental validation 2

Experimental validation (2)

  • SDR-GSC:

    • GSC (1/ = 0) : degraded performance if significant leakage

    • 1/ > 0 increases robustness (speech distortion  noise reduction)

  • SP-SDW-MWF:

    • No mismatch: same as SDR-GSC, larger due to post-filter

    • Performance is not degraded by mismatch

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions


Experimental validation 3

QIC  f(amount of speech leakage)  less noise reduction than SDR-GSC for small mismatch

SP-SDW-MWF achieves better noise reduction than QIC-GSC, for a given maximum speech distortion level

Experimental validation (3)

  • GSC with QIC ( ) : QIC increases robustness GSC

  • For large mismatch: less noise reduction than SP-SDW-MWF

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions


Audio demonstration

Audio demonstration

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

    -SP SDW MWF

    -Experimental validation

  • Implementation

  • Conclusions


Low cost implementation 1

Low-cost implementation (1)

  • Algorithms (in decreasing order of complexity):

    • GSVD-based – chic et très cher

    • QRD-based, fast QRD-based – chic et moins cher

    • Stochastic gradient algorithms – chic et pas cher

  • Stochastic gradient algorithm (time-domain) :

    • Cost function

      results in LMS-based updating formula

    • Allows transition to classical LMS-based GSC by tuning some parameters (1/,w0)

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

    -Stochastic gradient

    -Complexity and memory

  • Conclusions

Classical GSC

regularisation term


Low cost implementation 2

Low-cost implementation (2)

  • Stochastic gradient algorithm (time-domain):

    • Regularisation term is unknown

      • Store samples in memory buffer during speech-and-noise periods and approximate regularisation term

      • Better estimate of regularisation term can be obtained by smoothing (low-pass filtering)

  • Stochastic gradient algorithm (frequency-domain):

    • Block-based implementation: fast convolution and fast correlation

      Complexity reduction

      Tuning of  and 1/ per frequency

      Still large memory requirement due to data buffers

    • Approximation of regularisation term in FD allows to replace data buffers by correlation matrices memory + computational complexity reduction (cf. poster)

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

    -Stochastic gradient

    -Complexity and memory

  • Conclusions

Large buffer required


Complexity memory

Complexity comparable to FD implementation of QIC-GSC

Substantial memory reduction through FD-approximation

Complexity + memory

  • Parameters: N = 3(mics), M = 2 (a), M = 3 (b), L= 32, fs = 16kHz, Ly = 10000

  • Computational complexity:

  • Memory requirement:

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

    -Stochastic gradient

    -Complexity and memory

  • Conclusions


Conclusions

Spatially pre-processed SDW Multichannel Wiener Filter

Conclusions

  • Design of robust multimicrophone noise reduction algorithm:

    • Design of robust fixed spatial preprocessor

       need for statistical information about microphones

    • Design of robust adaptive stage

       take speech distortion into account in cost function

  • SP-SDW-MWF encompasses GSC and MWF as special cases

  • Experimental results:

    • SP-SDW-MWF achieves better noise reduction than QIC-GSC, for a given maximum speech distortion level

    • Filter w0improves performance in presence of model errors

  • Implementation: stochastic gradient algorithms available at affordable complexity and memory

  • Introduction

  • Robust spatial

    pre-processor

  • Adaptive stage

  • Implementation

  • Conclusions


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