313 research outputs found

    A New Variable Regularized QR Decomposition-Based Recursive Least M-Estimate Algorithm-Performance Analysis and Acoustic Applications

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    A new regularized QRD recursive least M-estimate algorithm: Performance analysis and applications

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    Proceedings of the International Conference on Green Circuits and Systems, 2010, p. 190-195This paper proposes a new regularized QR decomposition based recursive least M-estimate (R-QRRLM) adaptive filtering algorithm and studies its mean and mean square convergence performance and application to acoustic echo cancellation (AEC). The proposed algorithm extends the conventional RLM algorithm by imposing a weighted L2 regularization term on the coefficients to reduce the variance of the estimator. Moreover, a QRD-based algorithm is employed for efficient recursive implementation and improved numerical property. The mean convergence analysis shows that a bias solution to the classical Wiener solution will be introduced due to the regularization. The steady-state excess mean square error (EMSE) is derived and it suggests that the variance will decrease while the bias will increase with the regularization parameter. Therefore, regularization can help to trade bias for variance. In this study, the regularization parameter can be adaptively selected and the resultant variable regularization parameter QRRLM (VR-QRRLM) algorithm can obtain both high immunity to input variation and low steady-state EMSE values. The theoretical results are in good agreement with simulation results. Computer simulation results on AEC show that the R-QRRLM and VR-QRRLM algorithms considerably outperform the traditional RLS algorithm when the input signal level is low or during double talk. © 2010 IEEE.published_or_final_versio

    A New State-Regularized QRRLS Algorithm with Variable Forgetting Factor

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    A New Variable Regularized Transform Domain NLMS Adaptive Filtering Algorithm-Acoustic Applications and Performance Analysis

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    Recursive Parametric Frequency/Spectrum Estimation for Nonstationary Signals With Impulsive Components Using Variable Forgetting Factor

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    Underdetermined-order recursive least-squares adaptive filtering: The concept and algorithms

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    Compressive Wave Computation

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    This paper considers large-scale simulations of wave propagation phenomena. We argue that it is possible to accurately compute a wavefield by decomposing it onto a largely incomplete set of eigenfunctions of the Helmholtz operator, chosen at random, and that this provides a natural way of parallelizing wave simulations for memory-intensive applications. This paper shows that L1-Helmholtz recovery makes sense for wave computation, and identifies a regime in which it is provably effective: the one-dimensional wave equation with coefficients of small bounded variation. Under suitable assumptions we show that the number of eigenfunctions needed to evolve a sparse wavefield defined on N points, accurately with very high probability, is bounded by C log(N) log(log(N)), where C is related to the desired accuracy and can be made to grow at a much slower rate than N when the solution is sparse. The PDE estimates that underlie this result are new to the authors' knowledge and may be of independent mathematical interest; they include an L1 estimate for the wave equation, an estimate of extension of eigenfunctions, and a bound for eigenvalue gaps in Sturm-Liouville problems. Numerical examples are presented in one spatial dimension and show that as few as 10 percents of all eigenfunctions can suffice for accurate results. Finally, we argue that the compressive viewpoint suggests a competitive parallel algorithm for an adjoint-state inversion method in reflection seismology.Comment: 45 pages, 4 figure

    Subband Adaptive Modeling of Digital Hearing Aids

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    In this thesis, the application of a subband adaptive model to characterize compression behaviour of five digital hearing aids is investigated. Using a signal-to-error ratio metric, modeling performance is determined by varying the number of analysis bands in the subband structure as well as consideration of three adaptive algorithms. The normalized least mean-squares (NLMS), the affine projection algorithm (APA), and the recursive least-squares (RLS) algorithms are employed using a range of parameters to determine the impact on modeling performance. Using the subband adaptive model to estimate the time-varying frequency response of each hearing aid allows the Perceptual Evaluation of Speech Quality (PESQ) mean-opinion score (MOS) to be computed. The PESQ MOS facilitates an estimation of a subjective assessment of speech quality using an objective score. Initial results suggest the PESQ MOS score is able to differentiate speech processed by hearing aids allowing them to be ranked accordingly. Further work is required to obtain subjective assessments of the processed speech signals and determine if possible correlations exist

    On the robustness of inverse scattering for penetrable, homogeneous objects with complicated boundary

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    The acoustic inverse obstacle scattering problem consists of determining the shape of a domain from measurements of the scattered far field due to some set of incident fields (probes). For a penetrable object with known sound speed, this can be accomplished by treating the boundary alone as an unknown curve. Alternatively, one can treat the entire object as unknown and use a more general volumetric representation, without making use of the known sound speed. Both lead to strongly nonlinear and nonconvex optimization problems for which recursive linearization provides a useful framework for numerical analysis. After extending our shape optimization approach developed earlier for impenetrable bodies, we carry out a systematic study of both methods and compare their performance on a variety of examples. Our findings indicate that the volumetric approach is more robust, even though the number of degrees of freedom is significantly larger. We conclude with a discussion of this phenomenon and potential directions for further research.Comment: 24 pages, 9 figure
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