122 research outputs found
Sparse nonlinear optimization for signal processing and communications
This dissertation proposes three classes of new sparse nonlinear optimization algorithms for network echo cancellation (NEC), 3-D synthetic aperture radar (SAR) image reconstruction, and adaptive turbo equalization in multiple-input multiple-output (MIMO) underwater acoustic (UWA) communications, respectively.
For NEC, the proposed two proportionate affine projection sign algorithms (APSAs) utilize the sparse nature of the network impulse response (NIR). Benefiting from the characteristics of lā-norm optimization, affine projection, and proportionate matrix, the new algorithms are more robust to impulsive interferences and colored input than the conventional adaptive algorithms.
For 3-D SAR image reconstruction, the proposed two compressed sensing (CS) approaches exploit the sparse nature of the SAR holographic image. Combining CS with the range migration algorithms (RMAs), these approaches can decrease the load of data acquisition while recovering satisfactory 3-D SAR image through lā-norm optimization.
For MIMO UWA communications, a robust iterative channel estimation based minimum mean-square-error (MMSE) turbo equalizer is proposed for large MIMO detection. The MIMO channel estimation is performed jointly with the MMSE equalizer and the maximum a posteriori probability (MAP) decoder. The proposed MIMO detection scheme has been tested by experimental data and proved to be robust against tough MIMO channels. --Abstract, page iv
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
Collaborative adaptive filtering for machine learning
Quantitative performance criteria for the analysis of machine learning architectures
and algorithms have long been established. However, qualitative performance criteria,
which identify fundamental signal properties and ensure any processing preserves the
desired properties, are still emerging. In many cases, whilst offline statistical tests
exist such as assessment of nonlinearity or stochasticity, online tests which not only
characterise but also track changes in the nature of the signal are lacking. To that end,
by employing recent developments in signal characterisation, criteria are derived for
the assessment of the changes in the nature of the processed signal.
Through the fusion of the outputs of adaptive filters a single collaborative hybrid
filter is produced. By tracking the dynamics of the mixing parameter of this filter,
rather than the actual filter performance, a clear indication as to the current nature of
the signal is given. Implementations of the proposed method show that it is possible to
quantify the degree of nonlinearity within both real- and complex-valued data. This is
then extended (in the real domain) from dealing with nonlinearity in general, to a more
specific example, namely sparsity. Extensions of adaptive filters from the real to the
complex domain are non-trivial and the differences between the statistics in the real
and complex domains need to be taken into account. In terms of signal characteristics,
nonlinearity can be both split- and fully-complex and complex-valued data can be
considered circular or noncircular. Furthermore, by combining the information obtained
from hybrid filters of different natures it is possible to use this method to gain a more
complete understanding of the nature of the nonlinearity within a signal. This also
paves the way for building multidimensional feature spaces and their application in
data/information fusion.
To produce online tests for sparsity, adaptive filters for sparse environments are
investigated and a unifying framework for the derivation of proportionate normalised
least mean square (PNLMS) algorithms is presented. This is then extended to derive
variants with an adaptive step-size. In order to create an online test for noncircularity,
a study of widely linear autoregressive modelling is presented, from which a proof of
the convergence of the test for noncircularity can be given. Applications of this method
are illustrated on examples such as biomedical signals, speech and wind data
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
System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis
We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem.
We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system.
The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.PhDCommittee Chair: Biing-Hwang Juang; Committee Member: Brani Vidakovic; Committee Member: David V. Anderson; Committee Member: Jeff S. Shamma; Committee Member: Xiaoli M
Distributed Signal Processing Algorithms for Wireless Networks
Distributed signal processing algorithms have become a key approach for statistical inference in wireless networks and applications such as wireless sensor networks and smart grids. It is well known that distributed processing techniques deal with the extraction of information from data collected at nodes that are distributed over a geographic area. In this context, for each specific node, a set of neighbor nodes collect their local information and transmit the estimates to a specific node. Then, each specific node combines the collected information together with its local estimate to generate an improved estimate. In this thesis, novel distributed cooperative algorithms for inference in ad hoc, wireless sensor networks and smart grids are investigated. Low-complexity and effective algorithms to perform statistical inference in a distributed way are devised. A number of innovative approaches for dealing with node failures, compression of data and exchange of information are proposed and summarized as follows: Firstly, distributed adaptive algorithms based on the conjugate gradient (CG) method for distributed networks are presented. Both incremental and diffusion adaptive solutions are considered. Secondly, adaptive link selection algorithms for distributed estimation and their application to wireless sensor networks and smart grids are proposed. Thirdly, a novel distributed compressed estimation scheme is introduced for sparse signals and systems based on compressive sensing techniques. The proposed scheme consists of compression and decompression modules inspired by compressive sensing to perform distributed compressed estimation. A design procedure is also presented and an algorithm is developed to optimize measurement matrices. Lastly, a novel distributed reduced-rank scheme and adaptive algorithms are proposed for distributed estimation in wireless sensor networks and smart grids. The proposed distributed
scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reducedādimension parameter vector
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