slide1
Download
Skip this Video
Download Presentation
Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2

Loading in 2 Seconds...

play fullscreen
1 / 20

Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2 - PowerPoint PPT Presentation


  • 78 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Simon Doclo 1 , Ann Spriet 1,2 , Marc Moonen 1 , Jan Wouters 2' - laken


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

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
ad