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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