Advanced digital signal processing and noise reduction

advanced digital signal processing and noise reduction second edition and also dsp noise reduction algorithm
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Published Date:15-07-2017
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Arenberg Doctoral School of Science, Engineering & Technology Faculty of Engineering Department of Electrical Engineering Digital signal processing algorithms for noise reduction, dynamic range compression, and feedback cancellation in hearing aids Kim Ngo Dissertation presented in partial fulfillment of the requirements for the degree of Doctor in Engineering July 2011Digital signal processing algorithms for noise reduction, dynamic range compression, and feedback cancellation in hearing aids Kim Ngo Jury: Dissertation presented in partial Prof. em. dr. ir. Y. Willems, chairman fulfillment of the requirements for Prof. dr. ir. M. Moonen, promotor the degree of Doctor Prof. dr. J. Wouters, co-promotor in Engineering Prof. dr. ir. S. H. Jensen, co-promoter (Aalborg University, Denmark) Prof. dr. ir. S. Doclo (University of Oldenburg, Germany) Prof. dr. ir. W. Verhelst, assessor (Vrije Universiteit Brussel, Belgium) Prof. dr. ir. H. Van Hamme, assessor Prof. dr. ir. J. Vandewalle July 2011© Katholieke Universiteit Leuven – Faculty of Engineering Kasteelpaark Arenberg 1/2200, B-3001 Leuven (Belgium) Alle rechten voorbehouden. Niets uit deze uitgave mag worden vermenigvuldigd en/of openbaar gemaakt worden door middel van druk, fotocopie, microfilm, elektronisch of op welke andere wijze ook zonder voorafgaande schriftelijke toestemming van de uitgever. All rights reserved. No part of the publication may be reproduced in any form by print, photoprint, microfilm or any other means without written permission from the publisher. ISBN 978-94-6018-389-8 D/2011/7515/91Preface A five-yearjourney has come to an end and I am finally readyto write the preface for my PhD thesis. At this moment, I don’t have that much to say other than to express my gratitude and countless thanks to all of those who have helped me during my PhD. I would like to thank Prof. Marc Moonen for giving me the opportunity to join his research group and for the guidance. The support and feedback from my co- promotersProf. JanWoutersandProf. SørenHoldtJensenhaswithoutanydoubt been very helpful. This thesis has been build on a number of collaboration with other researchers and I would therefore like to send a special thank to Toon van Waterschoot and Ann Spriet. During the period of my research I visited Aalborg University and I would like to send a special gratitude to Prof. Mads Græsbøll Christensen who introduced me to pitch estimation which has shown to be very fruitful in my research. Another great experience was the opportunity to visit UniversityofIllinois atUrbana-champagneand especiallyProf. DouglasL. Jones. I would also like to thank the jury members: Prof. Simon Doclo, Prof. Werner Verhelst, Prof. Hugo Van Hamme, Prof. Joos Vandewalle, and Prof. Yves Willems (chairman) for their time, effort, and valuable comments and suggestions to improve my thesis. To the research group at the Katholieke Universiteit Leuven: Geert V.M., Geert R., Vincent, Paschal, Jan, Deepak, Pepe, Geert C., Alexander, Bruno, Beier, Amir, Rodrigo, Javi, Joseph, Sam, Guang, and Sylwester. Thank you all for the wonderful moments and discussions. Bram for being my Dutch translator when needed. To the people in the SIGNAL project: Mikael, Matthias, Pietro, Elena, Manya, Nuria, Li Jun, Jean-marc, and Johan thank you all for the many travels and courses that we had together. I would also like to thank David and Eric from UIUC who offered their help when I just arrived and they both made my visit morepleasant. A specialthanks to my goodfriends Prabin,Romain, and Daniele. iii I would also like to thank my family and friends in Denmark for supporting me through my PhD. Kim Ngo Leuven, July 2011Abstract Hearing loss can be caused by many factors, e.g., daily exposure to excessive noise in the work environment and listening to loud music. Another important reason can be age-related, i.e., the slow loss of hearing that occurs as people get older. In general hearing impaired people suffer from a frequency-dependent hearing loss and from a reduced dynamic range between the hearing threshold and the uncomfortable level. This means that the uncomfortable level for normal hearingand hearingimpairedpeoplesuffering fromso calledsensorineuralhearing loss remains the same but the hearing threshold and the sensitivity to soft sounds are shifted as a result of the hearing loss. To compensate for this kind of hearing loss the hearing aid should include a frequency-dependent and a level-dependent gain. Thecorrespondingdigitalsignalprocessing(DSP)algorithmisreferredtoas dynamic range compression (DRC). Background noise (from competing speakers, traffic etc.) is also a significant problem for hearing impaired people who indeed havemore difficulty understanding speech in noise and so in generalneed a higher signal-to-noise-ratio (SNR) than people with normal hearing. Because of this the noise reduction (NR) is also an important algorithmic component in hearing aids. Another issue in hearing aids is the undesired acoustic coupling between the loudspeaker and the microphone which is referred to as the acoustic feedback problem. Acoustic feedback produces an annoying howling sound and limits the maximum amplification that can be used in the hearing aid without making it unstable. To tackle the acoustic feedback problem adaptive feedback cancellation (AFC)algorithmsareused. Acousticfeedbackisbecominganevenmoresignificant problem due to the use of open fittings and the decreasing distance between the microphone and the loudspeaker. In this thesis several DSP techniques are presented to address the problems introduced above. For the backgroundnoise problem, we propose a NR algorithm based on the speech distortion weighted multi-channel Wiener filter (SDW-MWF) that is designed to allow for a trade-off between NR and speech distortion. The firstcontributiontotheSDW-MWFbasedNRisbasedonusingaweightingfactor that is updated for each frequency and for each frame such that speech dominant segments and noise dominant segments can be weighted differently. This can be iiiiv done by incorporating the conditional speech presence probability (SPP) in the SDW-MWF. The second contribution is based on an alternative and more robust method to estimate and update the correlation matrices, which is very important since an SDW-MWF based NR is uniquely based on these correlation matrices. The proposed SDW-MWF based NR shows better performance in terms of SNR improvement and signal distortion compared to a traditional SDW-MWF. For the problem of background noise and reduced dynamic range, we propose a combined algorithm of an SDW-MWF based NR and DRC. First the DRC is extended to a dual-DRC approach that allows for a switchable compression char- acteristic based on the conditional SPP. Secondly the SDW-MWF incorporating the conditional SPP is combined and analysed together with the dual-DRC. The proposed method shows that the SNR degradation can be partially controlled by using the dual-DRC. For the acoustic feedback problem, we propose a prediction error method based AFC (PEM-based AFC) exploiting an improved cascaded near-end signal model. The challenge in PEM-based AFC is to accurately estimate the near-end signal model such that the inverse of this model can be used as a decorrelation of the loudspeaker and the microphone signals. Due to the closed signal loop the loudspeaker and the microphone signal are now correlated which causes standard adaptivefilteringmethods tofail. TheproposedPEM-basedAFCshowsimproved performance in terms of maximum stable gain (MSG) and filter misadjustment compared to a PEM-based AFC using a single near-end signal model.Korte Inhoud Gehoorverlies kan worden veroorzaakt door vele factoren, voorbeelden zijn dagelijkse blootstelling aan overmatig lawaai in de werkomgeving of luisteren naar luide muziek. Een andere belangrijke reden is gerelateerd aan de leeftijd, met name de langzame achteruitgang van het gehoor die optreedt als mensen ouder worden. In het algemeen lijden slechthorenden aan een frequentie- afhankelijk gehoorverlies en aan een verminderd dynamisch bereik tussen de gehoordrempelenhetoncomfortabeleniveau. Ditbetekentdathetoncomfortabele niveau voor normaalhorenden en slechthorenden, die lijden aan een zogenaamd sensorineuraalverlies, hetzelfde blijft, terwijl de gehoordrempelen de gevoeligheid voor zachte geluiden worden verschoven ten gevolge van het gehoorverlies. Ter compensatievoorditsoortvangehoorverliesmoethethoorapparaateenfrequentie- afhankelijke en niveau-afhankelijke versterking toepassen. Het corresponderende digitale signaalverwerkingsalgoritme(DSP) is het zogenaamde Dynamisch Bereik Compressie-algoritme (DRC). Achtergrondgeluiden (van door elkaar pratende personen, verkeer enz.) vormen ook een groot probleem voor slechthorenden, die inderdaad meer moeite hebben met spraakverstaan in ruis en over het algemeen dus behoefte hebben aan een hogere signaal-ruisverhouding (SNR) dan normaal- horenden. Hierdoor kan ruisonderdrukking (NR) ook worden beschouwd als een belangrijke algoritmische component in hoorapparaten. Een ander probleem in hoorapparaten is de ongewenste akoestische koppeling tussen de luidspreker en de microfoon, die wordt aangeduid als het akoestische terugkoppelings- of feedbackprobleem. Akoestische terugkoppeling produceert een irritant fluitend geluid en beperkt de maximale versterking die in het hoortoestel kan worden toegepast zonder dat het onstabiel wordt. Ter bestrijding van het akoestische terugkoppelingsprobleem worden Adaptieve Feedbackonderdrukkingsalgoritmes (AFC)gebruikt. Akoestischeterugkoppelingisrecentelijkeennoggroterprobleem gewordendoorhetgebruikvanopenaanpassingenendeafnemendeafstandtussen de microfoon en de luidspreker. In dit proefschrift worden verschillende DSP-technieken gepresenteerd om de problemen aan te pakken die hierboven werden ge¨ıntroduceerd. Voor het achtergrondgeluid probleem, stellen we een NR algoritme voor dat is gebaseerd vvi op de spraak distortie gewogen meerkanaals Wiener filter (SDW-MWF), die ontworpen is om een afweging tussen NR en spraak distortie mogelijk te maken. De eerste bijdrage aan de SDW-MWF gebaseerde NR is gebaseerd op het gebruik van eenwegingsfactor,die wordtbijgewerktvoorelke frequentieen voorelk frame, zodanig dat spraak-dominante segmenten en ruis-dominante segmenten op een verschillende manier kunnen gewogen worden. Dit kan gedaan worden door het opnemen van de voorwaardelijke kans op spraak aanwezigheid (SPP) in de SDW- MWF. De tweedebijdrageis gebaseerdopeen alternatieveen robuusteremethode omcorrelatiematricesteschattenenbijtewerken,watheelbelangrijkisaangezien de SDW-MWF gebaseerde NR enkel gebruik maakt van deze correlatie matrices. De voorgesteldeSDW-MWF gebaseerdeNR toont betere prestaties in termen van SNR verbetering en spraak distortie, vergeleken met een traditionele SDW-MWF. Voorhetprobleemvanachtergrondlawaaienverminderddynamischbereik,stellen we een combinatie van SDW-MWF gebaseerde NR en DRC voor. Eerst wordt de DRC uitgebreid met een duale DRC benadering die een omschakeling van de compressie karakteristiek op basis van de voorwaardelijke SPP toelaat. Ten tweede wordt de SDW-MWF met voorwaardelijkeSPP samen met de duale DRC gecombineerd en geanalyseerd. De voorgestelde methode toont aan dat de SNR degradatie gedeeltelijk kan worden gecontroleerd met behulp van de duale DRC. Voor het akoestische terugkoppelingsprobleem, stellen we een Predictie Fout Methode-gebaseerde AFC (PEM-gebaseerde AFC) voor, waarbij een verbeterd gecascadeerd bronsignaalmodel wordt aangewend. De uitdaging in PEM- gebaseerde AFC is een nauwkeurige schatting van het bronsignaalmodel te bekomen zodat de inverse van dit model gebruikt kan worden als decorrelatie van de luidspreker en de microfoonsignalen. Door de gesloten signaallus zijn de luidsprekerendemicrofoonsignalennugecorreleerdwaardoorstandaardadaptieve filtering methodes mislukken. De voorgestelde PEM-gebaseerde AFC toont verbeterde prestaties in termen van maximale stabiele versterking (MSG) en filter misaanpassing, vergeleken met een PEM-gebaseerde AFC met een enkelvoudig bronsignaalmodel.Nomenclature Mathematical Notation a scalar a a vectora A matrix A T T A , a transpose of matrix A, vectora H H A , a Hermitian transpose of matrix A, vector a ˆ ˆ aˆ, a, A estimate of scalar a, vector a, matrix A. ε· expectation operator Tr· trace operator · absolute value k·k 2-norm t discrete time variable ∈ element of C set of complex numbers ω radial frequency variable (rad) log common logarithm 10 max(·) maximum min(·) minimum exp(·) exponential operator Fixed Symbols d(t) feedback compensated signal e l-th canonical vector l f sampling frequency s f feedback path impulse response vector ˆ f(t) estimated feedback path impulse response vector viiviii F(q,t) feedback path model H(q,t) near-end signal model k frequency bin index l frame index M number of microphones n feedback path model order F r(t) source excitation signal T reverberation time 60 u(t) loudspeaker signal x(t) microphone signal s X (k,l) speech component in the i-th microphone i n X (k,l) noise component in the i-th microphone i X (k,l) i-th microphone signal i s X (k,l) stacked speech vector n X (k,l) stacked noise vector s X (k,l) stacked data vector v(t) near-end signal W(k,l) stacked filter vector of multi-channel noise reduction y(t) microphone signal Z(k,l) output of the noise reduction algorithm μ weighting factor to trade-off between noise reduction and speech distortion α exponential weighting factor for the noise correlation matrice n α exponential weighting factor for the speech-plus-noise correla- x tion matrice ε(t) prediction error Acronyms and Abbreviations AFC Adaptive Feedback Cancellation AR autoregressive BTE Behind-the-ear CIC Completely-in-the-canal CPZLP Constrained Pole-Zero Linear Prediction dB Decibels DRC Dynamic Range Compression DSP Digital Signal Processing e.g. exempli gratia: for example etc. et cetera: and so forth FFT Fast Fourier Transform FIR Finite Impulse Response GSC Generalized Sidelobe Cancellerix HRTF Head-Related Transfer Function Hz hertz i.e. id est: that is IFFT Inverse Fast Fourier Transform IIR Infinite Impulse Response ITC In-the-canal ITE In-the-ear kHz kilohertz LMS Least Mean Squares LP Linear Prediction ms milliseconds MMSE Minimum Mean Square Error MSG Maximum Stable Gain MVDR Minimum Variance Distortionless Response MWF Multi-channel Wiener Filter NIHL Noise-induced hearing loss NR Noise Reduction PEM Prediction Error Method PEM-AFC PEM-based AFC PZLP Pole-Zero Linear Prediction RCB Robust Capon Beamformer SAP Speech Absence Probability SCB Standard Capon Beamformer SD Signal Distortion SFM Spectral Flatness Measure SDW-MWF Speech Distortion Weighted MWF SPL Sound Pressure Level SNR Signal-to-Noise-Ratio SPP Speech Presence Probability STFT Short-Time Fourier Transform vs. versus VAD Voice Activity DetectionContents Contents xi 1 Introduction 1 1.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 Hearing impairment . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Some statistics . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.3 Commercial hearing aids . . . . . . . . . . . . . . . . . . . . 7 1.1.4 Characterization of signals. . . . . . . . . . . . . . . . . . . 8 1.1.5 Acoustic environment . . . . . . . . . . . . . . . . . . . . . 9 1.1.6 Reduced dynamic range . . . . . . . . . . . . . . . . . . . . 10 1.1.7 Acoustic feedback . . . . . . . . . . . . . . . . . . . . . . . 11 1.1.8 Signal processing challenges . . . . . . . . . . . . . . . . . . 11 1.2 Noise reduction in hearing aids . . . . . . . . . . . . . . . . . . . . 13 1.2.1 Single-channel noise reduction. . . . . . . . . . . . . . . . . 15 1.2.2 Multi-channel noise reduction . . . . . . . . . . . . . . . . . 17 1.3 Dynamic range compression in hearing aids . . . . . . . . . . . . . 21 1.3.1 Design of DRC algorithms . . . . . . . . . . . . . . . . . . . 21 1.3.2 Perceptual benefits from DRC . . . . . . . . . . . . . . . . 22 1.4 Feedback cancellation in hearing aids . . . . . . . . . . . . . . . . . 24 1.4.1 Feedforward suppression . . . . . . . . . . . . . . . . . . . . 25 xixii CONTENTS 1.4.2 Feedback cancellation . . . . . . . . . . . . . . . . . . . . . 25 1.4.3 Bias problem and decorrelation . . . . . . . . . . . . . . . . 26 1.5 Outline of the thesis and main contributions . . . . . . . . . . . . . 28 1.5.1 Main research objectives . . . . . . . . . . . . . . . . . . . . 28 1.5.2 Chapter by chapter outline and contributions . . . . . . . . 28 2 Speech distortion weighted multi-channel Wiener filter (SDW-MWF ) 33 μ 2.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.1.1 Estimation of correlation matrices . . . . . . . . . . . . . . 35 2.2 Multi-channel Wiener filter (MWF). . . . . . . . . . . . . . . . . . 36 2.3 Speech distortion weighted MWF (SDW-MWF ) . . . . . . . . . . 38 μ 2.4 Rank-1 SDW-MWF . . . . . . . . . . . . . . . . . . . . . . . . . . 39 μ 2.5 Analysis of the SDW-MWF . . . . . . . . . . . . . . . . . . . . . 40 μ 2.5.1 Robustness and tracking . . . . . . . . . . . . . . . . . . . . 42 2.6 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 2.6.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 43 2.6.2 Performance measures . . . . . . . . . . . . . . . . . . . . . 44 2.6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3 SDW-MWF based on speech presence probability (SPP) 49 μ 3.1 Conditional speech presence probability (SPP) . . . . . . . . . . . 50 3.1.1 Multi-channel a priori and a posteriori SNR estimation . . 51 3.1.2 A priori speech absence probability (SAP) estimation . . . 52 3.2 SDW-MWF incorporating the conditional SPP (SDW-MWF ) . 53 SPP 3.2.1 Derivation of SDW-MWF . . . . . . . . . . . . . . . . . 54 SPP 3.2.2 Combined solution . . . . . . . . . . . . . . . . . . . . . . . 55 3.3 SDW-MWFincorporatingaflexibleweightingfactor(SDW-MWF ) 56 FlexCONTENTS xiii 3.4 Rank-1 SDW-MWF incorporating the conditional SPP . . . . . . . 60 3.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.5.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 62 3.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4 SDW-MWF based on robust estimation of the correlation matrices 67 μ 4.1 Robust estimation of the correlation matrices . . . . . . . . . . . . 68 4.1.1 Uncertainty of the correlation matrices . . . . . . . . . . . . 68 4.1.2 Continuous updating of the correlation matrices . . . . . . 69 4.1.3 Selection of prior correlation matrices . . . . . . . . . . . . 70 4.2 Analysis of estimation errors . . . . . . . . . . . . . . . . . . . . . 71 4.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.3.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 72 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5 Robust Capon beamforming for small arrays 83 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2 Standard Capon Beamforming (SCB) . . . . . . . . . . . . . . . . 85 5.2.1 Optimization criterion for SCB . . . . . . . . . . . . . . . . 85 5.2.2 Mismatch between presumed and actual steering vector . . 85 5.3 Previous work on robust Capon beamformers . . . . . . . . . . . . 85 5.3.1 Linearly constrained minimum variance . . . . . . . . . . . 86 5.3.2 Diagonal-loading-based beamformer . . . . . . . . . . . . . 86 5.3.3 Uncertainty-based beamformer . . . . . . . . . . . . . . . . 86 5.3.4 Max-min optimization . . . . . . . . . . . . . . . . . . . . . 87 5.4 Robust Capon beamforming (RCB). . . . . . . . . . . . . . . . . . 89xiv CONTENTS 5.4.1 Proposed RCB formulation . . . . . . . . . . . . . . . . . . 89 5.4.2 Gradient update of the steering vector . . . . . . . . . . . . 91 5.4.3 Computational complexity. . . . . . . . . . . . . . . . . . . 92 5.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.5.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 93 5.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 6 Dynamic range compression (DRC) 101 6.1 Design of DRC algorithms . . . . . . . . . . . . . . . . . . . . . . . 102 6.1.1 Multi-band compression . . . . . . . . . . . . . . . . . . . . 102 6.1.2 DRC parameters . . . . . . . . . . . . . . . . . . . . . . . . 103 6.2 The effect of background noise on DRC . . . . . . . . . . . . . . . 105 6.2.1 Undesired amplification over frequencies . . . . . . . . . . . 105 6.2.2 Undesired amplification over time . . . . . . . . . . . . . . 107 6.2.3 Compensation of speech and noise dominant segments . . . 108 6.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 109 6.3.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 109 6.3.2 Analysis procedure . . . . . . . . . . . . . . . . . . . . . . . 110 6.3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 7 SDW-MWF based noise reduction and dynamic range compression 119 7.1 Problem statement and motivation . . . . . . . . . . . . . . . . . . 120 7.2 Combined SDW-MWF based NR and DRC . . . . . . . . . . . . 122 μ 7.3 Combined SDW-MWF based NR and dual-DRC . . . . . . . . . 124 spp 7.4 Combined SDW-MWF based NR and flex dual-DRC . . . . . . 127 flex 7.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 128CONTENTS xv 7.5.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 128 7.5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 8 Prediction error method-based adaptive feedback cancellation 135 8.1 Adaptive feedback cancellation (AFC) . . . . . . . . . . . . . . . . 136 8.1.1 Prediction error method . . . . . . . . . . . . . . . . . . . . 137 8.2 Single near-end signal model . . . . . . . . . . . . . . . . . . . . . 138 8.3 Cascaded near-end signal model . . . . . . . . . . . . . . . . . . . . 138 8.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 139 8.4.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 139 8.4.2 Performance measures . . . . . . . . . . . . . . . . . . . . . 140 8.4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 9 PEM-based AFC using a harmonic sinusoidal near-end signal model 143 9.1 Harmonic sinusoidal near-end signal model . . . . . . . . . . . . . 144 9.1.1 Optimal-filtering based pitch estimation . . . . . . . . . . . 144 9.1.2 Subspace-orthogonality based pitch estimation . . . . . . . 145 9.1.3 Subspace-shift-invariance based pitch estimation . . . . . . 146 9.1.4 Amplitude and models order estimation . . . . . . . . . . . 146 9.2 PZLP using pitch estimation based PEF . . . . . . . . . . . . . . . 147 9.2.1 Incorporating amplitude, order and pitch information . . . 147 9.3 Voiced-unvoiced detection . . . . . . . . . . . . . . . . . . . . . . . 149 9.3.1 ZCR and energy based voiced-unvoiced detection . . . . . . 150 9.3.2 Spectral flatness of the residual . . . . . . . . . . . . . . . . 151 9.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 152 9.4.1 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 153xvi CONTENTS 9.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 10 Conclusion and further research 161 10.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 10.1.1 Noise reduction . . . . . . . . . . . . . . . . . . . . . . . . . 161 10.1.2 Combined noise reduction and dynamic range compression 163 10.1.3 Feedback cancellation . . . . . . . . . . . . . . . . . . . . . 164 10.2 Suggestions for further research . . . . . . . . . . . . . . . . . . . . 165 10.2.1 Noise reduction . . . . . . . . . . . . . . . . . . . . . . . . . 165 10.2.2 Combined noise reduction and dynamic range compression 166 10.2.3 Feedback cancellation . . . . . . . . . . . . . . . . . . . . . 167 Bibliography 169 List of publications 191 Curriculum vitae 193

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