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

On Improvement of Proportionate Adaptive Algorithms for Sparse Impulse Response

高知工科大学博士(工学) 平成21年9月30日授与 (甲第168号

Sparse Distributed Learning Based on Diffusion Adaptation

This article proposes diffusion LMS strategies for distributed estimation over adaptive networks that are able to exploit sparsity in the underlying system model. The approach relies on convex regularization, common in compressive sensing, to enhance the detection of sparsity via a diffusive process over the network. The resulting algorithms endow networks with learning abilities and allow them to learn the sparse structure from the incoming data in real-time, and also to track variations in the sparsity of the model. We provide convergence and mean-square performance analysis of the proposed method and show under what conditions it outperforms the unregularized diffusion version. We also show how to adaptively select the regularization parameter. Simulation results illustrate the advantage of the proposed filters for sparse data recovery.Comment: to appear in IEEE Trans. on Signal Processing, 201

Journal of Telecommunications and Information Technology, 2022, nr 3

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Learning classifier systems from first principles: A probabilistic reformulation of learning classifier systems from the perspective of machine learning

Learning Classifier Systems (LCS) are a family of rule-based machine learning methods. They aim at the autonomous production of potentially human readable results that are the most compact generalised representation whilst also maintaining high predictive accuracy, with a wide range of application areas, such as autonomous robotics, economics, and multi-agent systems. Their design is mainly approached heuristically and, even though their performance is competitive in regression and classification tasks, they do not meet their expected performance in sequential decision tasks despite being initially designed for such tasks. It is out contention that improvement is hindered by a lack of theoretical understanding of their underlying mechanisms and dynamics.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Computational Methods for Analyzing Health News Coverage

Researchers that investigate the media's coverage of health have historically relied on keyword searches to retrieve relevant health news coverage, and manual content analysis methods to categorize and score health news text. These methods are problematic. Manual content analysis methods are labor intensive, time consuming, and inherently subjective because they rely on human coders to review, score, and annotate content. Retrieving relevant health news coverage using keywords can be challenging because manually defining an optimal keyword query, especially for complex health topics and media analysis concepts, can be very difficult, and the optimal query may vary based on when the news was published, the type of news published, and the target audience of the news coverage. This dissertation research investigated computational methods that can assist health news investigators by facilitating these tasks. The first step was to identify the research methods currently used by investigators, and the research questions and health topics researchers tend to investigate. To capture this information an extensive literature review of health news analyses was performed. No literature review of this type and scope could be found in the research literature. This review confirmed that researchers overwhelmingly rely on manual content analysis methods to analyze the text of health news coverage, and on the use of keyword searching to identify relevant health news articles. To investigate the use of computational methods for facilitating these tasks, classifiers that categorize health news on relevance to the topic of obesity, and on their news framing were developed and evaluated. The obesity news classifier developed for this dissertation outperformed alternative methods, including searching based on keyword appearance. Classifying on the framing of health news proved to be a more difficult task. The news framing classifiers performed well, but the results suggest that the underlying features of health news coverage that contribute to the framing of health news are a richer and more useful source of framing information rather than binary news framing classifications. The third step in this dissertation was to use the findings of the literature review and the classifier studies to design the SalientHealthNews system. The purpose of SalientHealthNews is to facilitate the use of computational and data mining techniques for health news investigation, hypothesis testing, and hypothesis generation. To illustrate the use of SalientHealthNews' features and algorithms, it was used to generate preliminary data for a study investigating how framing features vary in health and obesity news coverage that discusses populations with health disparities. This research contributes to the study of the media's coverage of health by providing a detailed description of how health news is studied and what health news topics are investigated, then by demonstrating that certain tasks performed in health news analyses can be facilitated by computational methods, and lastly by describing the design of a system that will facilitate the use of computational and data mining techniques for the study of health news. These contributions should further the study of health news by expanding the methods available to health news analysis researchers. This will lead to researchers being better equipped to accurately and consistently evaluate the media's coverage of health. Knowledge of the quality of health news coverage should in turn lead to better informed health journalists, healthcare providers, and healthcare consumers, ultimately improving individual and public health

Background Examples of Literature Searches on Topics of Interest

A zip file of various literature searches & some resources related to our work related to exposure after the Chernobyl accident and as we began looking at helping in Semey Kazakhstan----a collection of literature reviews on various topics we were interested in... eg. establishing a registry of those exposed for longterm follow-up, what we knew about certain areas like genetics and some resources like A Guide to Environmental Resources on the Internet by Carol Briggs-Erickson and Toni Murphy which could be found on the Internet and was written to be used by researchers, environmentalists, teachers and any person who is interested in knowing and doing something about the health of our planet. See more at https://archives.library.tmc.edu/dm-ms211-012-0060