References

Oppenheim AV, Schafer RW, Buch JR (2011) Discrete-time signal processing. Prentice Hall,New Jersey

Mitra SK (2010) Digital signal processing a computer based approach. Mc Graw-Hill, New YorkÐurovic ZM, Kovacevic BD (2004) Digitalni signali i sistemi: pregled teorije i rešeni zadaci.

Akademska misao, BeogradWidrow B, Stearns SD (1985) Adaptive signal processing. Prentice Hall, New JerseyHaykin S (2012) Adaptive filther theory. Englewood Cliffs, Prentice HallCowan C, Grant P (1985) Adaptive filters. Englewood cliffs, Prentice HallGelb A (1974) Applied optimal estimation. MIT Press, CambridgeKovacevic B, Filipovic V (1998) Robust real-time identification of linear systems with correlated

noise. Int J Control 48(3):993–1010Sage AP, Melsa JL (1971) Estimation theory with applications to communications and control.

McGraw Hill, New YorkKovacevic BD, Ðurovic ZM (2008) Fundamentals of stochastic signals, Systems and estimation

theory with worked examples. Springer, BerlinBar-Shalom Y, Li XR (1998) Estimation and tracking: principles techniques and software. CRC,

DanversBard Y (1974) Nonlinear parameter estimation. Academic Press, New YorkWilcox RR (1997) Introduction to robust estimation and hypothesis testing. Academic Press, New

YorkHuber P (1980) Robust statistics. Wiley, New YorkKovacevic DB (1984) Robust recursive system parameter identification, PhD Thesis, University

of Belgrade (in Serbian)Van Trees HL (2001) Detection, estimation and modulation theory, part I-IV. Wiley, New YorkMurano K, Unagami S, Amano F (1990) Echo cancellation and applications. IEEE Commun Mag

28(1):49–55Messerschmitt DG (1984) Echo cancellation in speech and data transmission. IEEE J Sel Areas

Commun SAC-2(2):283–296Veseghi SV (2006) Advanced signal processing and digital noise reduction. Wiley, New YorkBanjac Z, Veinovic M, Kovacevic B, Milosavljevic M (2002) An application of adaptive FIR

filter with nonlinear optimal input design. In: 14th international conference on digital signalprocessing, Santorini

Banjac Z, Kovacevic B, Ðurovic Z, Milosavljevic M (1988) A class of algorithms for local echocancellation using optimal input design. In: Proceedings of MELECON ‘98, vol I, Tel-Aviv,Israel, pp 1376–1379

Haykin S (1998) Neural networks: a comprehensive foundation. Prentice Hall, Upper SaddleRiver

Fan H, Jenkis W (1988) An investigation of an adaptive IIR echo canceller: advantages andproblems. IEEE Trans Acoust Speech Sig Process 36(12):1819–1834

B. Kovacevic et al., Adaptive Digital Filters,DOI: 10.1007/978-3-642-33561-7,� Academic Mind Belgrade and Springer-Verlag Berlin Heidelberg 2013

205

Ljung L, Soderstrom T (1983) Theory and practice of recursive identification. MIT Press,Cambridge

Ljung L, Soderstrom T (1987) System identification: theory for the user. Prentice-Hall,Englewood Cliffs

Solo V, Kong X (1999) Adaptive signal processing algorithms—stability and performance.Prentice Hall, New Jersey

Widrow B (1976) Stationary and nonstationary learning characteristic of LMS adaptive filters.Proc IEEE 64(8):1–151

Tsypkin YZ (1984) Foundations of informational theory of identification. Nauka, MoscowSayed AH (2003) Fundamentals of adaptive filtering. Wiley, NJUzunovic P, Banjac Z, Kovacevic B (2004) Adaptive IIR filtering: advances and problems. In:

Proceedings of the international conference on telecomunications, TELFOR, BelgradeCho YS, Kim SB, Powers EJ (1991) Time varying spectral estimation using ar models with

variable forgetting factors. IEEE Trans Sig Process 39(6):1422–1426Fortescue TR, Kershenbaum LS, Ydstie BE (1981) Implementation of self-tuning regulators with

variable forgetting factors. Automatica 17(6):831–835Bard J (1974) Nonlinear parameter estimation. Academic Press, New YorkDelgado JC, Tribolet JM (1984) Analog full-duplex speech scrambling systems. IEEE J Sel Areas

Commun SAC-2(3):456–489Hampel F (1974) The influence curve and its role in robust estimation. J Amer Stat Assoc 69:383Chambers J, Tanrikulu O, Constantinides AG (1994) Least mean mixed-norm adaptive filtering.

Electron Lett 30:1574–1575Eleftheriou E, Falconer DD (1986) Tracking properties and steady-state performance of RLS

adaptive filter algorithms. IEEE Trans Acoust Speech Sig Process 34:1097–1109Peters SD, Antoniou A (1995) A parallel adaptation algorithm for recursive least squares adaptive

filters in nonstationary environments. IEEE Trans Sig Process 43(11):2484–2494Willems JL (1970) Stability theory of dynamical systems. Nelson, Walton-on-ThamesGlentis GO, Berberidis K, Theodoridis S (1999) Efficient least squares adaptive filtering for FIR

transversal filtering. Sig Process Mag 16(4):13–41Mareels IMY, Bitmead RR, Gevers M, Johnson CR, Kosut RL, Poubelle MA (1987) How

exciting can a signal really be. Syst Control Lett 8:197–204Goodwin GC, Payne RL (1977) Dynamic system identification: experiment design and data

analysis. Academic Press, New YorkGoodwin GC (1987) Identification: experiment design. In: Sing M (ed) Encyclopaedia of systems

and control. Pergamon Press, Oxford, pp 2257–2264Tsypkin YZ (1983) Optimality in identification of linear plants. Int J Syst Sci 14(1):59–74Bard Y (1974) Nonlinear parameter estimation. Academic Press, New YorkBarkat M (2005) Signal detection and estimation. Artech House, LondonKovacevic B, Ðurovic Z, Filipovic V (1996) Robust system identification using optimal input

signal design. J Autom Control Univ Belgrade 6(1):19–29Soderstrom T (1973) An on-line algorithm for approximate maximum likelihood identification of

linear dynamic systems, Rep 7308, Lund Institute of TechnologyGoodwin GC, Sin KS (1984) Adaptive filtering, prediction and control. Prentice Hall, New JerseyPoularikas AD, Ramadan ZM (2006) Adaptive filtering primer with MATLAB. CRC Press,

Taylor and Francis, Boca RatonBanjac ZÐ, Kovacevic BD, Milosavljevic MM, Veinovic MÐ (2002) An adaptive FIR filter for

echo cancelling using least squares with nonlinear input design. Control Intell Syst30(1):27–31

Banjac ZÐ, Kovacevic BD, Milosavljevic MM, Veinovic MÐ (2002) Local echo canceller withoptimal input for true full-duplex speech scrambling system. IEEE Trans Sig Process50(5):1877–1882

Becker H, Piper F (1982) Cipher systems: the protection of communications. Northwood Books,London

206 References

Cox RV, Tribolet JM (1983) Analog voice privacy systems using TFSP scrambling. Bell SysTech J 62:47–61

Park S, Hillman G (1989) On acoustic echo cancellation implementation with multiplecascadable adaptive FIR filter chips. Proc ICASSP 2:952–955

Kovacevic B, Milosavljevic M, Veinovic M, Markovic M (2004) Robust digital speechprocessing. Academic Mind, Belgrade (in Serbian)

Chambers J, Avlonitis A (1997) A robust mixed-norm adaptive filter algorithm. IEEE Sig ProcessLett 4(2):46–48

Astrom K (1980) Maximum likelihood and prediction error methods. Automatica 16:551Barnett V, Lewis T (1978) Outliers in statistical data. Wiley, New YorkWilliamson GA, Clarkson PM, Sethares WA (1993) Performance characteristics of median LMS

adaptive filter. IEEE Trans Sig Process 41:667–680Banjac ZÐ, Kovacevic BD, Veinovic MÐ, Milosavljevic MM (2001) Robust least mean square

adaptive FIR filter algorithm. IEE Proc Vision Image Sig Proc 148(5):332–336Banjac Z, Milosavljevic M, Kovacevic B, Veinovic M (1998) Robust RLS algorithm for local

echo cancellation using optimal input design. In: Proceedings of the 1st internationalconference on digital signal processing and its applications DSPA-98, vol 1, Moscow, Russia,pp 225–231

Banjac ZÐ, Kovacevic BD (2005) Robust parameter and scale factor estimation in nonstationaryimpulsive noise environment. In: Proceedings of theIEEE conference, EUROCON 2005,Belgrade, Serbia and Montenegro

Banjac ZÐ, Kovacevic BD, Veinovic MÐ, Milosavljevic MM (2004) Robust adaptive filteringwith variable forgetting factor. WSEAS Trans Circ Syst 3(2):223–229

Sondihi D, Berkly A (1980) Silencing echoes on the telephone network. In: Proceedings IEEE,vol. 68, Aug 1980

Tao YG, Kolwicz K, Gritton CWK, Duttweiler DI (1984) A cascadable VLSI echo canceller.IEEE J Sel Areas Commun SAC-2(2):297–303

Yaukawa H, Furukawa I, Ishiyama Y (1989) Acoustic echo control for high quality audioteleconferencing. In: Proceedings of ICASP 89, vol 2, Glasgow, Scotland, pp 2041–2044,23–26 May 1989

Tanrikulu O, Baykal B, Constantinides AG, Chambers JA (April 1997) Residual echo signal incritically sampled subband acoustic echo cancellers based on IIR and FIR filter banks. IEEETrans Sig Process 45:901–912

Kuo M, Pan Z (1994) Development and analyses of distributed acoustic echo cancellationmicrophone system. Sig Process 37(3)

Verhoeckx NAM, van den Elzen HC, Snijders FAM, van Gerwen PJ (1979) Digital echocancellation for baseband data transmission. IEEE Trans Acoust Speech Sig Process ASSP27(6)

Falconer D, Mueller KH (1979) Adaptive echo cancellation/AGC structures for two-wire, full-duplex data transmission. Bell Syst Tech J 58(7)

Yip PCW, Etter DM (1990) An adaptive multiple echo canceller for slowly time-varying echopaths. IEEE Trans Commun 38(10)

Medvecky M (1996) Modified NLMS algorithm for acoustic echo cancellation. In: ProceedingsIWISP 96, Manchester, UK, pp 683–686, 4–7 Nov 1996

Chen J, Vandevalle J (1989) Study of nonlinear echo canceller with voltera expansion. In:Proceedings of ICASSP ‘89, vol. 2, Glasgow, Scotland, pp 1376–1379, 23–26 May 1989

Moon TK, Stirling WC (2000) Mathematical methods and algorithms in signal processing.Prentice-Hall, NJ

References 207

Index

AAcoustic echo, 188, 191, 194Adaptive algorithms, 27–29, 42, 45, 59–63Adaptive digital filters, 38, 39, 60, 62Adaptive echo suppression, 188, 193–197Aliasing, 2, 8Amplification matrix, 92, 115Amplitude characteristics, 4, 5Angular frequency, 2Arithmetic mean, 41, 48Asymptotic error covariance

matrix (AECM), 112Auto-correlation matrix, 38

CCausality, 3, 54, 60, 67, 71, 73Chebyshev, 3–7, 200Convergence properties, 48, 62, 65, 148, 162,

197Convergence speed, 29, 30, 45, 106, 112, 139,

148, 153, 156, 197, 198, 200Covariant matrix, 79, 84, 89Cross-correlation, 10, 22

vector, 38, 43

EEcho, 187–189Echo canceller (EC), 131–134Echo return loss enhancement (ERLE), 136,

137, 139, 198–203Efficiently robust, 152Equation error (EE), 60–63, 67Error signal, 31, 37, 46, 49, 54, 59, 66Estimation, 78, 88, 89, 91, 94, 98, 100, 109,

112, 115–117, 121–124, 129–131,135–146, 149–154, 164, 165, 175–176,179, 194, 194, 197–198, 201–204

Excitation signal, 1, 2, 26, 36, 54, 60, 83–85,117, 134, 139, 197–203

Extended prediction error (EPE), 76–78,92, 182

FFilter

coefficients, 36, 48, 60–62memory length, 76, 81order, 75, 86, 89, 101, 130, 131, 158, 168,

184, 185, 203, 204Finite impulse esponse (FIR), 3, 28, 29, 35–37,

45–53, 59–61, 78, 90, 101–105, 109,110, 117, 119–121, 123, 134–140, 149,150, 156, 158, 163, 168, 180, 185, 196,198, 200, 203

Fisher’s information amount, 156Fortescue, Kershenbaum

and Ydstie (FKY), 78, 81, 86, 96,103–108, 182

Forgetting factor, 53, 54, 57, 58, 60, 67,75–81, 86–96, 100–106, 139, 140, 167,168, 181–186

Frequency response, 5, 8Full-duplex, 131, 132, 138

GGauss–Newton, 63Global minimum, 33, 39, 59, 61Gradient, 39–48, 65, 67–72, 118, 119, 121,

123, 130, 149, 150, 153, 163, 171, 202estimation, 37

HHessian, 64, 65, 118, 119, 122, 123Huber, 152, 156, 163, 178

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Huber’s nonlinearity, 148, 171, 176, 178Hybrid, 133, 189–195

IIdentification, 110, 123, 133, 140, 147–150,

158, 180, 184Impulse interference, 139, 149, 152, 163Impulse noise, 139, 148, 149, 151, 152, 154,

155, 157–162, 166, 168, 170, 177–183Infinite impulse response (IIR), 3, 29, 32–39,

59–66, 71, 84, 109, 134, 197, 200–204Influence function, 152, 155, 157, 163,

178, 183Iteration, 91, 101, 109, 110, 136–139, 154,

176, 182, 185

KKalman filter, 16, 17, 21–26Kramer–Rao, 27

LLag error, 87Laplace distribution, 151Laplace transform, 7, 10, 14Least absolute deviation (LAD), 151, 158Least mean square (LMS), 46–49, 53, 62, 67,

109, 148–154, 157–162, 202Least squares algorithm, 48Line echo, 133, 193, 195Local echo, 131, 132, 134, 136, 188, 193–195,

197, 204Lyapunov, 115, 124, 128–131, 155

MMathematical expectation, 84, 88, 93, 117,

118, 122, 126, 127, 136, 163, 171Maximal likelihood, 147, 150, 151, 156, 161,

170Mean-square criterion, 117Mean square error (MSE), 10–13, 27, 37,

39–44, 47, 62, 64, 67, 72, 88, 90–92,109, 111, 117, 122, 123, 131–134,162, 197

Median, 77, 78, 149, 153, 178, 182Median least mean square (MLMS), 149, 154,

158–162Median of absolute deviation (MAD), 153,

154, 178M-estimator, 150, 152, 170Mixed normal distribution, 151

ML estimator, 150, 156Monotonicity, 152

NNewton–Raphson, 163, 164, 171Newton’s method, 135, 146, 168, 169Noise variance, 77, 179, 180Nonstationarity, 75–77, 87, 88, 91–93,

101–103, 145, 183Nonstationary environments, 75, 78, 87, 139,

180Normal distribution, 134Normalized estimation error (NEE), 135, 136,

139, 140, 145, 146, 158, 169, 170, 179Normalized information matrix, 112, 113Normalized least mean square algorithm

(NLMS), 51, 198

OOptimal filters, 9Optimal input, 112–115, 132, 134, 136, 138,

140, 146Optimal output, 109, 110, 117, 133, 134, 150,

166–169, 188Ordinary differential equations (ODE), 115, 124Outlier, 147–149, 151, 158, 162, 182, 184–186Output eror (OE), 60, 62, 66–68, 71

PParallel adaptation (PA), 92, 96, 101, 103, 104,

106, 108, 140, 181, 182Parallel adaptation recursive least square

(PA-RLS), 86, 92, 140–146, 181, 182,185, 186

Parameteridentification, 101, 109, 123, 148, 180update, 33, 73vector, 37–40, 42, 45–47, 52, 53, 60,

62–66, 68, 69, 110–118, 112, 126,131–133, 145, 149, 154, 162, 16.3, 168,171, 174, 183

Prediction error, 76, 77, 85, 92, 98, 105, 111,117, 118, 121, 124, 130

Probability density, 38, 97, 150, 152, 156, 157Pseudo-linear regression (PLR), 72Pseudo-random binary sequence (Prbs), 135

RRandom disturbance, 147Random walk (RW), 87–88

210 Index

Recursive least square (RLS), 46, 51, 53–58,62, 72, 75, 78, 82, 87, 89, 91, 96, 101,102, 131, 134, 139–141, 162, 165, 169,178, 179, 182

Recursive prediction error (RPE), 67–71Residual, 21, 23–25, 27, 53, 58, 64, 76, 78, 80,

85, 87, 93, 94, 98–100, 117, 122,148–150, 152, 154, 162–166, 176, 178,182

cutoff, 152Risk function, 118Robust, 147, 148, 155, 161, 162, 165, 166,

170, 178estimation, 147–149, 151, 165, 178

Robustification, 147, 152Robust least mean square (RLMS), 150, 151,

154–157Robust mixed norm (RMN), 149, 151,

158–162Robust recursive least square (RRLS), 148,

149, 162–168, 178–182, 184, 186RRLS algorithm with optimal input (RRLSO),

148, 166–170

SScaling factor, 117, 123, 153, 156, 157, 170,

172, 174–181Scrambler, 131, 133Signal to noise ratio (SNR), 77, 83, 103, 104,

134–138, 140–146, 168, 177Steepest descent method, 42–47Structures of digital filters, 31

TTransfer function, 3, 6–10, 12–16, 27, 31, 32,

34–36, 45, 72Time constant, 55, 58, 76, 104, 105

UUnbiased estimation, 84Unit impulse, 32, 36

VVariable forgetting factor (VFF), 75, 76,

100–107, 140, 141, 145, 168

WWeighted recursive least square (WRLS),

53–57, 60, 67, 79, 87Weight matrix, 117White normal noise, 134, 145Wiener, 9, 11, 15, 16, 26Wiener–Hopf, 41

ZZeroes

filter, 32, 34–36polynomial, 4

Index 211