Nico De Clercq Pieter Gijsenbergh

ProblemSolutions

Single-channel approach Multichannel approach

Our assignment

Overview

Speech is a highly redundant signal: Normal people: noise not a big problem Hearing impaired: noise reduces

intelligibility

Noise = any unwanted signal that interferes with the desired signal

Assumption: additive, locally stationary noise

Problem

ProblemSolutions

Single-channel approach Multichannel approach

Our assignment

Overview

Noise-cancelling microphonesVoice processor modifications

Preprocessor noise reduction Single-channel Multichannel

Solutions

Single-channel noise reductionOnly one device captures the

signal: Only spectral and temporal characteristics

Techniques: Wiener-filtering Spectral-subtracting Sine-wave modelling Directional microphones

Optimal adaptive filter to maximize SNR

Problem: noise and signal have to be known Solution: use short-term spectra

speech more or less constant

Difficult approach & internal noise issues

Single-channel: Wiener-filter

Principle Measure noise spectrum in non-speech

activity Take mean of measured amplitudes Subtract mean from input signal

Spectral error

Single-channel: spectral subtraction (1)

( ) ( ) ( )j j jX e S e N e jxj ej j jS e X e e e

xjj j je N e e e

Single-channel: spectral subtraction (2)

Modifications: magnitude averaging, half-wave rectification, residual noise reduction, …

Expected results: noise reduced, equal intelligibility

Explanation: non-stationary noise!

ProblemSolutions

Single-channel approach Multichannel approach

Our assignment

Overview

Multiple sensors capture signal: Exploits spatial diversity of the noise

Noise and signal almost always differ in location

In hearing aids Noise microphone Speech + noise microphone Adaptive filtering

Multi-channel noise reduction

Constructive and deconstructive interference Controls phase (delay) & relative

amplitude (constraint)Fixed or adaptive

Multi-channel: Beamforming

Delay-sum beamformers Inputs are weighed (phase shift)

Filter-sum beamformers Amplitude & phase weights frequency

dependant

Multi-channel: Beamforming (1)

1

( ) ( ) ( )N

n nn

y f w f x f

1

1( ) ( )

N

n nn

y t x tN

Superdirective beamformers Maximize array gain, suppress noise

from other directions Near field superdirectivity for good low

frequency performance Amplitude + phase

Multi-channel: Beamforming (2)

Fixed beam former: Points to desired signal Mostly filter-sum beam formers used

Blocking Matrix (B): Separates desired signal from noise: rows add

up to 0 Maximum N-1 rows

Adaptive part: Minimizes the noise power in the output LMS, with frequency domain processing:

Multi-channel: Beamforming (3) Generalized Sidelobe Canceller

k kf fx ''( ) = B x '( ) ( )kf f f y f k+1 k ka ( ) a ( ) x ''( )

Multi-channel: Beamforming (4) Generalized Sidelobe Canceller

x´´

ProblemSolutions

Single-channel approach Multichannel approach

Our assignment

Overview

Implement & test algorithmOur choice:

Generalized Sidelobe Canceller with LMS update

Frequency domain implementation of LMS

DSP II: overlap-add, adaptive filtering, time and frequency domain, multirate, …

Our assignment

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

https://gilbert.med.kuleuven.be/~koen/demo_beam/demo_beam.html http://www.rp-photonics.com/interference.html

Reference

Questions

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