Nico De Clercq Pieter Gijsenbergh

 Problem  Solutions  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

 Problem  Solutions  Single-channel approach  Multichannel approach  Our assignment Overview

 Noise-cancelling microphones  Voice processor modifications  Preprocessor noise reduction  Single-channel  Multichannel Solutions

 Only 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)

 Modifications: magnitude averaging, half- wave rectification, residual noise reduction, …  Expected results: noise reduced, equal intelligibility  Explanation: non-stationary noise!

 Problem  Solutions  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)

 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

Multi-channel: Beamforming (4) Generalized Sidelobe Canceller x´´

 Problem  Solutions  Single-channel approach  Multichannel approach  Our assignment Overview

 Implement & test algorithm  Our 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  J.J Shynk, "Frequency-domain and multirate adaptive filtering " Signal Processing Magazine, IEEE, Volume 9, Issue 1, Jan 1992 Page(s):  I. A. McCowan, “Robust Speech Recognition using Microphone Arrays”, PhD Thesis, Queensland University of Technology, Australia,  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   Reference

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