Locating and extracting acoustic and neural signals

This dissertation presents innovate methodologies for locating, extracting, and separating multiple incoherent sound sources in three-dimensional (3D) space; and applications of the time reversal (TR) algorithm to pinpoint the hyper active neural activities inside the brain auditory structure that are correlated to the tinnitus pathology. Specifically, an acoustic modeling based method is developed for locating arbitrary and incoherent sound sources in 3D space in real time by using a minimal number of microphones, and the Point Source Separation (PSS) method is developed for extracting target signals from directly measured mixed signals. Combining these two approaches leads to a novel technology known as Blind Sources Localization and Separation (BSLS) that enables one to locate multiple incoherent sound signals in 3D space and separate original individual sources simultaneously, based on the directly measured mixed signals. These technologies have been validated through numerical simulations and experiments conducted in various non-ideal environments where there are non-negligible, unspecified sound reflections and reverberation as well as interferences from random background noise. Another innovation presented in this dissertation is concerned with applications of the TR algorithm to pinpoint the exact locations of hyper-active neurons in the brain auditory structure that are directly correlated to the tinnitus perception. Benchmark tests conducted on normal rats have confirmed the localization results provided by the TR algorithm. Results demonstrate that the spatial resolution of this source localization can be as high as the micrometer level. This high precision localization may lead to a paradigm shift in tinnitus diagnosis, which may in turn produce a more cost-effective treatment for tinnitus than any of the existing ones

Adaptive explicit time delay, frequency estimations in communications systems

Ph.DDOCTOR OF PHILOSOPH

Spontaneous and explicit estimation of time delays in the absence/presence of multipath propagation.

by Hing-cheung So.Thesis (Ph.D.)--Chinese University of Hong Kong, 1995.Includes bibliographical references (leaves 133-141).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Time Delay Estimation (TDE) and its Applications --- p.1Chapter 1.2 --- Goal of the Work --- p.6Chapter 1.3 --- Thesis Outline --- p.9Chapter 2 --- Adaptive Methods for TDE --- p.10Chapter 2.1 --- Problem Description --- p.11Chapter 2.2 --- The Least Mean Square Time Delay Estimator (LMSTDE) --- p.11Chapter 2.2.1 --- Bias and Variance --- p.14Chapter 2.2.2 --- Probability of Occurrence of False Peak Weight --- p.16Chapter 2.2.3 --- Some Modifications of the LMSTDE --- p.17Chapter 2.3 --- The Adaptive Digital Delay-Lock Discriminator (ADDLD) --- p.18Chapter 2.4 --- Summary --- p.20Chapter 3 --- The Explicit Time Delay Estimator (ETDE) --- p.22Chapter 3.1 --- Derivation and Analysis of the ETDE --- p.23Chapter 3.1.1 --- The ETDE system --- p.23Chapter 3.1.2 --- Performance Surface --- p.26Chapter 3.1.3 --- Static Behaviour --- p.28Chapter 3.1.4 --- Dynamic Behaviour --- p.30Chapter 3.2 --- Performance Comparisons --- p.32Chapter 3.2.1 --- With the LMSTDE --- p.32Chapter 3.2.2 --- With the CATDE --- p.34Chapter 3.2.3 --- With the CRLB --- p.35Chapter 3.3 --- Simulation Results --- p.38Chapter 3.3.1 --- Corroboration of the ETDE Performance --- p.38Chapter 3.3.2 --- Comparative Studies --- p.44Chapter 3.4 --- Summary --- p.48Chapter 4 --- An Improvement to the ETDE --- p.49Chapter 4.1 --- Delay Modeling Error of the ETDE --- p.49Chapter 4.2 --- The Explicit Time Delay and Gain Estimator (ETDGE) --- p.52Chapter 4.3 --- Performance Analysis --- p.55Chapter 4.4 --- Simulation Results --- p.57Chapter 4.5 --- Summary --- p.61Chapter 5 --- TDE in the Presence of Multipath Propagation --- p.62Chapter 5.1 --- The Multipath TDE problem --- p.63Chapter 5.2 --- TDE with Multipath Cancellation (MCTDE) --- p.64Chapter 5.2.1 --- Structure and Algorithm --- p.64Chapter 5.2.2 --- Convergence Dynamics --- p.67Chapter 5.2.3 --- The Generalized Multipath Cancellator --- p.70Chapter 5.2.4 --- Effects of Additive Noises --- p.73Chapter 5.2.5 --- Simulation Results --- p.74Chapter 5.3 --- TDE with Multipath Equalization (METDE) --- p.86Chapter 5.3.1 --- The Two-Step Algorithm --- p.86Chapter 5.3.2 --- Performance of the METDE --- p.89Chapter 5.3.3 --- Simulation Results --- p.93Chapter 5.4 --- Summary --- p.101Chapter 6 --- Conclusions and Suggestions for Future Research --- p.102Chapter 6.1 --- Conclusions --- p.102Chapter 6.2 --- Suggestions for Future Research --- p.104Appendices --- p.106Chapter A --- Derivation of (3.20) --- p.106Chapter B --- Derivation of (3.29) --- p.110Chapter C --- Derivation of (4.14) --- p.111Chapter D --- Derivation of (4.15) --- p.113Chapter E --- Derivation of (5.21) --- p.115Chapter F --- Proof of unstablity of A°(z) --- p.116Chapter G --- Derivation of (5.34)-(5.35) --- p.118Chapter H --- Derivation of variance of αs11(k) and Δs11(k) --- p.120Chapter I --- Derivation of (5.40) --- p.123Chapter J --- Derivation of time constant of αΔ11(k) --- p.124Chapter K --- Derivation of (5.63)-(5.66) --- p.125Chapter L --- Derivation of (5.68)-(5.72) --- p.129References --- p.13

Split algorithms for LMS adaptive systems.

by Ho King Choi.Thesis (Ph.D.)--Chinese University of Hong Kong, 1991.Includes bibliographical references.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Adaptive Filter and Adaptive System --- p.1Chapter 1.2 --- Applications of Adaptive Filter --- p.4Chapter 1.2.1 --- System Identification --- p.4Chapter 1.2.2 --- Noise Cancellation --- p.6Chapter 1.2.3 --- Echo Cancellation --- p.8Chapter 1.2.4 --- Speech Processing --- p.10Chapter 1.3 --- Chapter Summary --- p.14References --- p.15Chapter 2. --- Adaptive Filter Structures and Algorithms --- p.17Chapter 2.1 --- Filter Structures for Adaptive Filtering --- p.17Chapter 2.2 --- Adaptation Algorithms --- p.22Chapter 2.2.1 --- The LMS Adaptation Algorithm --- p.24Chapter 2.2.1.1 --- Convergence Analysis --- p.28Chapter 2.2.1.2 --- Steady State Performance --- p.33Chapter 2.2.2 --- The RLS Adaptation Algorithm --- p.35Chapter 2.3 --- Chapter Summary --- p.39References --- p.41Chapter 3. --- Parallel Split Adaptive System --- p.45Chapter 3.1 --- Parallel Form Adaptive Filter --- p.45Chapter 3.2 --- Joint Process Estimation with a Split-Path Adaptive Filter --- p.49Chapter 3.2.1 --- The New Adaptive System Identification Configuration --- p.53Chapter 3.2.2 --- Analysis of the Split-Path System Modeling Structure --- p.57Chapter 3.2.3 --- Comparison with the Non-Split Configuration --- p.63Chapter 3.2.4 --- Some Notes on Even Filter Order Case --- p.67Chapter 3.2.5 --- Simulation Results --- p.70Chapter 3.3 --- Autoregressive Modeling with a Split-Path Adaptive Filter --- p.75Chapter 3.3.1 --- The Split-Path Adaptive Filter for AR Modeling --- p.79Chapter 3.3.2 --- Analysis of the Split-Path AR Modeling Structure --- p.84Chapter 3.3.3 --- Comparison with Traditional AR Modeling System --- p.89Chapter 3.3.4 --- Selection of Step Sizes --- p.90Chapter 3.3.5 --- Some Notes on Odd Filter Order Case --- p.94Chapter 3.3.6 --- Simulation Results --- p.94Chapter 3.3.7 --- Application to Noise Cancellation --- p.99Chapter 3.4 --- Chapter Summary --- p.107References --- p.109Chapter 4. --- Serial Split Adaptive System --- p.112Chapter 4.1 --- Serial Form Adaptive Filter --- p.112Chapter 4.2 --- Time Delay Estimation with a Serial Split Adaptive Filter --- p.125Chapter 4.2.1 --- Adaptive TDE --- p.125Chapter 4.2.2 --- Split Filter Approach to Adaptive TDE --- p.132Chapter 4.2.3 --- Analysis of the New TDE System --- p.136Chapter 4.2.3.1 --- Least-mean-square Solution --- p.138Chapter 4.2.3.2 --- Adaptation Algorithm and Performance Evaluation --- p.142Chapter 4.2.4 --- Comparison with Traditional Adaptive TDE Method --- p.147Chapter 4.2.5 --- System Implementation --- p.148Chapter 4.2.6 --- Simulation Results --- p.148Chapter 4.2.7 --- Constrained Adaptation for the New TDE System --- p.156Chapter 4.3 --- Chapter Summary --- p.163References --- p.165Chapter 5. --- Extension of the Split Adaptive Systems --- p.167Chapter 5.1 --- The Generalized Parallel Split System --- p.167Chapter 5.2 --- The Generalized Serial Split System --- p.170Chapter 5.3 --- Comparison between the Parallel and the Serial Split Adaptive System --- p.172Chapter 5.4 --- Integration of the Two Forms of Split Predictors --- p.177Chapter 5.5 --- Application of the Integrated Split Model to Speech Encoding --- p.179Chapter 5.6 --- Chapter Summary --- p.188References --- p.139Chapter 6. --- Conclusions --- p.191References --- p.19

The estimation and compensation of processes with time delays

The estimation and compensation of processes with time delays have been of interest to academics and practitioners for several decades. A full review of the literature for both model parameter and time delay estimation is presented. Gradient methods of parameter estimation, in open loop, in the time and frequency domains are subsequently considered in detail. Firstly, an algorithm is developed, using an appropriate gradient algorithm, for the estimation of all the parameters of an appropriate process model with time delay, in open loop, in the time domain. The convergence of the model parameters to the process parameters is considered analytically and in simulation. The estimation of the process parameters in the frequency domain is also addressed, with analytical procedures being defined to provide initial estimates of the model parameters, and a gradient algorithm being used to refine these estimates to attain the global minimum of the cost function that is optimised. The focus of the thesis is subsequently broadened with the consideration of compensation methods for processes with time delays. These methods are reviewed in a comprehensive manner, and the design of a modified Smith predictor, which facilitates a better regulator response than does the Smith predictor, is considered in detail. Gradient algorithms are subsequently developed for the estimation of process parameters (including time delay) in closed loop, in the Smith predictor and modified Smith predictor structures, in the time domain; the convergence of the model parameters to the process parameters is considered analytically and in simulation. The thesis concludes with an overview of the methods developed, and projections regarding future developments in the topics under consideration