A measurement method for the linear and nonlinear properties of electro-acoustic transmission systems

Heinle, F.; Rabenstein, Rudolf; Stenger, Alexander

doi:10.1016/s0165-1684(97)00175-8

Cited by 17 publications

(9 citation statements)

References 1 publication

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“…The results obtained in [5] agreed with the observations of several authors in ANC area [2][3][4]6]. The possibility of quantifying the nonlinear effects on the performance surface motivated the search for new algorithms that could be capable of overcoming the poor steady-state performance of the LMS algorithm in a nonlinear environment.…”

Section: I Introductionsupporting

confidence: 85%

“…Several adaptive modeling and control systems present saturation-type nonlinearities in the adaptive filter path [1][2][3][4][5][6]. Such nonlinearities can severely impair the adaptive algorithm performance in minimizing a cost function, usually the mean square output error (MSE) [5][6][7].…”

Section: I Introductionmentioning

confidence: 99%

“…A common assumption, for simplicity of analysis, is that the system is purely linear. However, it is well known that the associated hardware (power amplifiers, piezoelectric transducers and loudspeakers) is an important source of nonlinearities [2][3][4]. In order to avoid nonlinear distortions, the system is frequently overdesigned to keep signal power sufficiently low compared to the saturation levels.…”

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confidence: 99%

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Optimum leakage factor for the MOV-LMS algorithm in nonlinear modeling and control systems

Bermudez

Costa

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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This work studies the Minimum Output Variance Least Mean Square Estimator (MOV -LMS) when the output of the adaptive filter is constrained by a saturation-type nonlinearity. This situation is typical in several adaptive modeling and control systems where the associated hardware and transducers have a finite power handling capability. Analytical expressions are obtained for the behaviors of the mean weight vector and for mean square error for Gaussian inputs and slow learning. The optimum leakage factor is detenn ined, which provides an unbiased solution to the associated nonlinear mean square estimation. problem. Monte Carlo simulations show excellent agreemen t between model and simulations for transient and steady-state conditions. The results in this paper demonstrate that the MOV-LMS algorithm with the optimized leakage factor has a superior steady-state performance than the LMS algorithm in the studied nonlinear environment I, INTRODUCTIONSeveral adaptive modeling and control systems present saturation-type nonlinearities in the adaptive filter path [1-6]. Such nonlinearities can severely impair the adaptive algorithm performance in minimizing a cost function, usually the mean square output error (MSE) [5][6][7].Among the various existing adaptive solutions, the family of stochastic gradient-based algorithms (the LMS family) is a common choice due to its simplicity and robustness. Several works have studied the behavior of the LMS and its family in modeling and control applications, such as active noise control (ANC) [I]. A common assumption, for simplicity of analysis, is that the system is purely linear. However, it is well known that the associated hardware (power amplifiers, piezoelectric transducers and loudspeakers) is an important source of nonlinearities [2][3][4]. In order to avoid nonlinear distortions, the system is frequently overdesigned to keep signal power sufficiently low compared to the saturation levels.A recent work [5] studied the behavior of the LMS algorithm subjected to a nonlinear influence at the adaptive filter output. It was demonstrated that the mean converged weights corres pond to a biased solution with respect to the minimum of the MSE surface. This bias is a multiplicative scalar, which is a function of the system's degree of nonlinearity.The results obtained in [5] agreed with the observations of several authors in ANC area [2-4,6]. The possibility of quantifying the nonlinear effects on the performance surface motivated the search for new algorithms that could be capable of overcoming the poor steady-state performance of the LMS algorithm in a nonlinear environment. Such a solution would allow the use of cheaper amplifiers and transducers without a significant performance loss, therefore reducing implementation costs.Reference [7] proposed a new stochastic gradient-based algorithm, referred to here as the Nonlinear True-LMS algorithm. This algorithm is capable of reaching the minimum of the MSE surface, since the system's nonlinearity is incorporated in the weight update equ...

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Section: I Introductionsupporting

confidence: 85%

Section: I Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimum leakage factor for the MOV-LMS algorithm in nonlinear modeling and control systems

Bermudez

Costa

2002

IEEE International Conference on Acoustics Speech and Signal Processing

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“…N point frequency spectrum I , k ( p ) . At this point we proceed as in section 2, using sampled versions of ( I )- (3). Note that spectra of subsampled signals have M frequency points, while fullband signals are represented by N .…”

Section: Measuring the Psd Of Alias Components In The Error Signalsmentioning

confidence: 99%

“…The measurement program uses both the input and output signal of the implemented system to calculate N separate LTI transfer functions Lk,v(dn), v E [O..N-13 at M frequency points Q p = ,U . g. A standard measurement methodfor LTI-Systems is used that yields unbiased and consistent spectral estimates and isolates other distortions[3]. For convenience we will denote the measured transfer functions with Lk,v(,u).…”

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confidence: 99%

Measuring performance limits of subband adaptive systems

Stenger

Rabenstein

Weiss³

et al.

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284)

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We discuss a method to measure the convergence limits of general subband adaptive systems due to non-ideal filter banks. Aliasing caused in such filter bank presents a distortion to the subband adaptive system which forms a lower limit for the minimum mean squared error. The accuracy of the achievable model is given by the transfer function of the filter bank. To measure both aliasing and filter bank distortions, we employ the measurement technique by Heinle and Schussler (see Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Atlanta, GA, p.2754-57, 1996). The presented approach is applicable to a wide range of subband adaptive filter systems. Examples for the measured limits are presented

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Digital signal processing methods of global nonparametric frequency domain audio testing

Potchinkov¹

2005

Signal Processing

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A measurement method for the linear and nonlinear properties of electro-acoustic transmission systems

Cited by 17 publications

References 1 publication

Optimum leakage factor for the MOV-LMS algorithm in nonlinear modeling and control systems

Optimum leakage factor for the MOV-LMS algorithm in nonlinear modeling and control systems

Measuring performance limits of subband adaptive systems

Digital signal processing methods of global nonparametric frequency domain audio testing

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