3,870 research outputs found
A wavelet based partial update fast LMS/Newton algorithm
This paper studies a wavelet based partial update fast LMS/Newton algorithm. Different from the conventional fast LMS/Newton algorithm, the proposed algorithm first uses a shorter-order, partial Haar transform-based NLMS adaptive filter to estimate the peak position of the long, sparse channel impulse response, and then employs the fast LMS/Newton algorithm integrated with partial update technique to fulfill the rest convergence task. The experimental results demonstrate the proposed algorithm outperforms its conventional counterpart in convergence performance and possesses a significantly lower computational complexity. © 2005 IEEE.published_or_final_versio
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
System Identification with Applications in Speech Enhancement
As the increasing popularity of integrating hands-free telephony on mobile portable devices
and the rapid development of voice over internet protocol, identification of acoustic
systems has become desirable for compensating distortions introduced to speech signals
during transmission, and hence enhancing the speech quality. The objective of this research
is to develop system identification algorithms for speech enhancement applications
including network echo cancellation and speech dereverberation.
A supervised adaptive algorithm for sparse system identification is developed for
network echo cancellation. Based on the framework of selective-tap updating scheme
on the normalized least mean squares algorithm, the MMax and sparse partial update
tap-selection strategies are exploited in the frequency domain to achieve fast convergence
performance with low computational complexity. Through demonstrating how
the sparseness of the network impulse response varies in the transformed domain, the
multidelay filtering structure is incorporated to reduce the algorithmic delay.
Blind identification of SIMO acoustic systems for speech dereverberation in the
presence of common zeros is then investigated. First, the problem of common zeros is
defined and extended to include the presence of near-common zeros. Two clustering algorithms
are developed to quantify the number of these zeros so as to facilitate the study
of their effect on blind system identification and speech dereverberation. To mitigate such
effect, two algorithms are developed where the two-stage algorithm based on channel
decomposition identifies common and non-common zeros sequentially; and the forced
spectral diversity approach combines spectral shaping filters and channel undermodelling
for deriving a modified system that leads to an improved dereverberation performance.
Additionally, a solution to the scale factor ambiguity problem in subband-based blind system identification is developed, which motivates further research on subbandbased
dereverberation techniques. Comprehensive simulations and discussions demonstrate
the effectiveness of the aforementioned algorithms. A discussion on possible directions
of prospective research on system identification techniques concludes this thesis
Adaptive filters for sparse system identification
Sparse system identification has attracted much attention in the field of adaptive algorithms, and the adaptive filters for sparse system identification are studied. Firstly, a new family of proportionate normalized least mean square (PNLMS) adaptive algorithms that improve the performance of identifying block-sparse systems is proposed. The main proposed algorithm, called block-sparse PNLMS (BS-PNLMS), is based on the optimization of a mixed ℓ2,1 norm of the adaptive filter\u27s coefficients. A block-sparse improved PNLMS (BS-IPNLMS) is also derived for both sparse and dispersive impulse responses. Meanwhile, the proposed block-sparse proportionate idea has been extended to both the proportionate affine projection algorithm (PAPA) and the proportionate affine projection sign algorithm (PAPSA).
Secondly, a generalized scheme for a family of proportionate algorithms is also presented based on convex optimization. Then a novel low-complexity reweighted PAPA is derived from this generalized scheme which could achieve both better performance and lower complexity than previous ones. The sparseness of the channel is taken into account to improve the performance for dispersive system identification. Meanwhile, the memory of the filter\u27s coefficients is combined with row action projections (RAP) to significantly reduce the computational complexity.
Finally, two variable step-size zero-point attracting projection (VSS-ZAP) algorithms for sparse system identification are proposed. The proposed VSS-ZAPs are based on the approximations of the difference between the sparseness measure of current filter coefficients and the real channel, which could gain lower steady-state misalignment and also track the change in the sparse system --Abstract, page iv
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