410 research outputs found
Study of L0-norm constraint normalized subband adaptive filtering algorithm
Limited by fixed step-size and sparsity penalty factor, the conventional
sparsity-aware normalized subband adaptive filtering (NSAF) type algorithms
suffer from trade-off requirements of high filtering accurateness and quicker
convergence behavior. To deal with this problem, this paper proposes variable
step-size L0-norm constraint NSAF algorithms (VSS-L0-NSAFs) for sparse system
identification. We first analyze mean-square-deviation (MSD) statistics
behavior of the L0-NSAF algorithm innovatively in according to a novel
recursion form and arrive at corresponding expressions for the cases that
background noise variance is available and unavailable, where correlation
degree of system input is indicated by scaling parameter r. Based on
derivations, we develop an effective variable step-size scheme through
minimizing the upper bounds of the MSD under some reasonable assumptions and
lemma. To realize performance improvement, an effective reset strategy is
incorporated into presented algorithms to tackle with non-stationary
situations. Finally, numerical simulations corroborate that the proposed
algorithms achieve better performance in terms of estimation accurateness and
tracking capability in comparison with existing related algorithms in sparse
system identification and adaptive echo cancellation circumstances.Comment: 15 pages,15 figure
Subband Adaptive Modeling of Digital Hearing Aids
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
Robust Total Least Mean M-Estimate normalized subband filter Adaptive Algorithm for impulse noises and noisy inputs
When the input signal is correlated input signals, and the input and output
signal is contaminated by Gaussian noise, the total least squares normalized
subband adaptive filter (TLS-NSAF) algorithm shows good performance. However,
when it is disturbed by impulse noise, the TLS-NSAF algorithm shows the rapidly
deteriorating convergence performance. To solve this problem, this paper
proposed the robust total minimum mean M-estimator normalized subband filter
(TLMM-NSAF) algorithm. In addition, this paper also conducts a detailed
theoretical performance analysis of the TLMM-NSAF algorithm and obtains the
stable step size range and theoretical steady-state mean squared deviation
(MSD) of the algorithm. To further improve the performance of the algorithm, we
also propose a new variable step size (VSS) method of the algorithm. Finally,
the robustness of our proposed algorithm and the consistency of theoretical and
simulated values are verified by computer simulations of system identification
and echo cancellation under different noise models
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