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

Channel estimation scheme for 3.9G wireless communication systems using RLS algorithm

Main challenges for a terminal implementation are efficient realization of the receiver, especially for channel estimation (CE) and equalization. In this paper, training based recursive least square (RLS) channel estimator technique is presented for a long term evolution (LTE) single carrier-frequency division multiple access (SC-FDMA) wireless communication system. This CE scheme uses adaptive RLS estimator which is able to update parameters of the estimator continuously, so that knowledge of channel and noise statistics are not required. Simulation results show that the RLS CE scheme with 500 Hz Doppler frequency has 3 dB better performances compared with 1.5 kHz Doppler frequency

Direction set based Algorithms for adaptive least squares problems improvements and innovations.

The main objective of this research is to provide a mathematically tractable solutions to the adaptive filtering problem by formulating the problem as an adaptive least squares problem. This approach follows the work of Chen (1998) in his study of direction-set based CDS) adaptive filtering algorithm. Through the said formulation, we relate the DS algorithm to a class of projection method. Objektif utama penyelidikan ini ialah untuk menyediakan penyelesaian matematik yang mudah runut kepada masalah penurasan adaptif dengan memfonnulasikan masalah tersebut sebagai masalah kuasa dua terkecil adaptif. Pendekatan ini rnengikut hasil kerja oleh Chen (1998) dalam kajian beliau tentang algoritma penurasan adaptif berasaskan 'direction-set' (DS). Melalui fornulasi tersebut, kami menghubungkaitkan algoritma DS kepada satu kelas kaedah unjuran. Secara khususnya, versi rnudah aigoritma itu, iaitu algoritma 'Euclidean direction search' (EDS) ditunjukkan mempunyai hubungkait dengan satu kelas kaedah berlelaran yang dipanggil kaedah 'relaxation'. Penernuan ini rnembolehkan kami menambahbaik algoritma EDS kepada 'accelerated EDS' eli mana satu parameter pemecutan diperkenalkan untuk rnengoptirnumkan saiz langkah sernasa setiap pencarian garis

Computationally efficient distributed minimum wilcoxon norm

In the fields related to digital signal processing and communication, as system identification, noise cancellation, channel equalization, and beam forming Adaptive filters play an important role. In practical applications, the computational complexity of an adaptive filter is an important consideration. As it describes system reliability, swiftness to real time environment least mean squares (LMS) algorithm is widely used because of its low computational complexity (O (N)) and simplicity in implementation. The least squares algorithms, having general form as recursive least squares (RLS), conjugate gradient (CG) and Euclidean direction search (EDS), can converge faster and have lower steady-state mean square error (MSE) than LMS. However, for their high computational complexity (O (N2)) makes them unsuitable for many real-time applications. Therefore controlling of computational complexity is obtained by partial update (PU) method for adaptive filters. A partial update method is implemented to reduce the adaptive algorithm complexity by updating a fraction of the weight vector instead of the entire weight vector. An analysis of different PU adaptive filter algorithms is necessary, sufficient so meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. System is related to the partial update adaptive filter, but has distinct performance. It can be viewed as a PU adaptive filter, in that machine the deficient-length adaptive filter also updates part of the weight vector. However, it updates some part of the weight vector in every iteration. While the partial update adaptive filter updates a different part of the weight vector for each iteration

Adaptive techniques in signal processing and connectionist models

This thesis covers the development of a series of new methods and the application of adaptive filter theory which are combined to produce a generalised adaptive filter system which may be used to perform such tasks as pattern recognition. Firstly, the relevant background adaptive filter theory is discussed in Chapter 1 and methods and results which are important to the rest of the thesis are derived or referenced. Chapter 2 of this thesis covers the development of a new adaptive algorithm which is designed to give faster convergence than the LMS algorithm but unlike the Recursive Least Squares family of algorithms it does not require storage of a matrix with n2 elements, where n is the number of filter taps. In Chapter 3 a new extension of the LMS adaptive notch filter is derived and applied which gives an adaptive notch filter the ability to lock and track signals of varying pitch without sacrificing notch depth. This application of the LMS filter is of interest as it demonstrates a time varying filter solution to a stationary problem. The LMS filter is next extended to the multidimensional case which allows the application of LMS filters to image processing. The multidimensional filter is then applied to the problem of image registration and this new application of the LMS filter is shown to have significant advantages over current image registration methods. A consideration of the multidimensional LMS filter as a template matcher and pattern recogniser is given. In Chapter 5 a brief review of statistical pattern recognition is given, and in Chapter 6 a review of relevant connectionist models. In Chapter 7 the generalised adaptive filter is derived. This is an adaptive filter with the ability to model non-linear input-output relationships. The Volterra functional analysis of non-linear systems is given and this is combined with adaptive filter methods to give a generalised non-linear adaptive digital filter. This filter is then considered as a linear adaptive filter operating in a non-linearly extended vector space. This new filter is shown to have desirable properties as a pattern recognition system. The performance and properties of the new filter is compared with current connectionist models and results demonstrated in Chapter 8. In Chapter 9 further mathematical analysis of the networks leads to suggested methods to greatly reduce network complexity for a given problem by choosing suitable pattern classification indices and allowing it to define its own internal structure. In Chapter 10 robustness of the network to imperfections in its implementation is considered. Chapter 11 finishes the thesis with some conclusions and suggestions for future work.Science and Engineering Research Council; Dr W. Fitzgerald; Marconi

Single mode excitation in the shallow water acoustic channel using feedback control

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 1996The shallow water acoustic channel supports far-field propagation in a discrete set of modes. Ocean experiments have confirmed the modal nature of acoustic propagation, but no experiment has successfully excited only one of the suite of mid-frequency propagating modes propagating in a coastal environment. The ability to excite a single mode would be a powerful tool for investigating shallow water ocean processes. A feedback control algorithm incorporating elements of adaptive estimation, underwater acoustics, array processing and control theory to generate a high-fidelity single mode is presented. This approach also yields a cohesive framework for evaluating the feasibility of generating a single mode with given array geometries, noise characteristics and source power limitations. Simulations and laboratory waveguide experiments indicate the proposed algorithm holds promise for ocean experiments.Josko Catipovic funded my research for summer of 1992 on the Office of Naval Research Grant Number N00014-92-J-1661 and from June 1993 through August 1995 on Defense Advanced Research Projects Agency Grant Number MDA972-92-J- 1041. The Office of Naval Research Grant N00014-95-1-0362 to MIT supported the computer facilities used to do much of this work

Estimation and Calibration Algorithms for Distributed Sampling Systems

Thesis Supervisor: Gregory W. Wornell Title: Professor of Electrical Engineering and Computer ScienceTraditionally, the sampling of a signal is performed using a single component such as an analog-to-digital converter. However, many new technologies are motivating the use of multiple sampling components to capture a signal. In some cases such as sensor networks, multiple components are naturally found in the physical layout; while in other cases like time-interleaved analog-to-digital converters, additional components are added to increase the sampling rate. Although distributing the sampling load across multiple channels can provide large benefits in terms of speed, power, and resolution, a variety mismatch errors arise that require calibration in order to prevent a degradation in system performance. In this thesis, we develop low-complexity, blind algorithms for the calibration of distributed sampling systems. In particular, we focus on recovery from timing skews that cause deviations from uniform timing. Methods for bandlimited input reconstruction from nonuniform recurrent samples are presented for both the small-mismatch and the low-SNR domains. Alternate iterative reconstruction methods are developed to give insight into the geometry of the problem. From these reconstruction methods, we develop time-skew estimation algorithms that have high performance and low complexity even for large numbers of components. We also extend these algorithms to compensate for gain mismatch between sampling components. To understand the feasibility of implementation, analysis is also presented for a sequential implementation of the estimation algorithm. In distributed sampling systems, the minimum input reconstruction error is dependent upon the number of sampling components as well as the sample times of the components. We develop bounds on the expected reconstruction error when the time-skews are distributed uniformly. Performance is compared to systems where input measurements are made via projections onto random bases, an alternative to the sinc basis of time-domain sampling. From these results, we provide a framework on which to compare the effectiveness of any calibration algorithm. Finally, we address the topic of extreme oversampling, which pertains to systems with large amounts of oversampling due to redundant sampling components. Calibration algorithms are developed for ordering the components and for estimating the input from ordered components. The algorithms exploit the extra samples in the system to increase estimation performance and decrease computational complexity