28,046 research outputs found
Proportionate Recursive Maximum Correntropy Criterion Adaptive Filtering Algorithms and their Performance Analysis
The maximum correntropy criterion (MCC) has been employed to design
outlier-robust adaptive filtering algorithms, among which the recursive MCC
(RMCC) algorithm is a typical one. Motivated by the success of our recently
proposed proportionate recursive least squares (PRLS) algorithm for sparse
system identification, we propose to introduce the proportionate updating (PU)
mechanism into the RMCC, leading to two sparsity-aware RMCC algorithms: the
proportionate recursive MCC (PRMCC) algorithm and the combinational PRMCC
(CPRMCC) algorithm. The CPRMCC is implemented as an adaptive convex combination
of two PRMCC filters. For PRMCC, its stability condition and mean-square
performance were analyzed. Based on the analysis, optimal parameter selection
in nonstationary environments was obtained. Performance study of CPRMCC was
also provided and showed that the CPRMCC performs at least as well as the
better component PRMCC filter in steady state. Numerical simulations of sparse
system identification corroborate the advantage of proposed algorithms as well
as the validity of theoretical analysis
Sparseness-controlled adaptive algorithms for supervised and unsupervised system identification
In single-channel hands-free telephony, the acoustic coupling between the loudspeaker and
the microphone can be strong and this generates echoes that can degrade user experience.
Therefore, effective acoustic echo cancellation (AEC) is necessary to maintain a stable
system and hence improve the perceived voice quality of a call. Traditionally, adaptive
filters have been deployed in acoustic echo cancellers to estimate the acoustic impulse
responses (AIRs) using adaptive algorithms. The performances of a range of well-known
algorithms are studied in the context of both AEC and network echo cancellation (NEC).
It presents insights into their tracking performances under both time-invariant and time-varying
system conditions.
In the context of AEC, the level of sparseness in AIRs can vary greatly in a mobile
environment. When the response is strongly sparse, convergence of conventional
approaches is poor. Drawing on techniques originally developed for NEC, a class of time-domain
and a frequency-domain AEC algorithms are proposed that can not only work
well in both sparse and dispersive circumstances, but also adapt dynamically to the level
of sparseness using a new sparseness-controlled approach.
As it will be shown later that the early part of the acoustic echo path is sparse
while the late reverberant part of the acoustic path is dispersive, a novel approach to
an adaptive filter structure that consists of two time-domain partition blocks is proposed
such that different adaptive algorithms can be used for each part. By properly controlling
the mixing parameter for the partitioned blocks separately, where the block lengths are
controlled adaptively, the proposed partitioned block algorithm works well in both sparse
and dispersive time-varying circumstances.
A new insight into an analysis on the tracking performance of improved proportionate
NLMS (IPNLMS) is presented by deriving the expression for the mean-square error.
By employing the framework for both sparse and dispersive time-varying echo paths, this
work validates the analytic results in practical simulations for AEC.
The time-domain second-order statistic based blind SIMO identification algorithms,
which exploit the cross relation method, are investigated and then a technique with proportionate
step-size control for both sparse and dispersive system identification is also
developed
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