Application Research of HHT-IF Speech Feature Parameter in Speaker Recognition System

AbstractIntroduced the Hilbert-Huang transform (HHT) algorithm for nonlinear and non-stationary signal analysis. Specially to non-stationary speech signals, a new method of extracting the speech feature parameters is offered based on the HHT. The speaker identification system is designed based on the VQ and the experiments are carried out at different situations with both HHT-IF and LPCC. The results show that the HHT-IF is feasible for speaker recognition

Characterizing Data Streams Over IEEE 802.11b ad-hoc Wireless Networks

Soon, advancements in data encryption technology will make real-time decryption of the contents of network packets virtually impossible. This research anticipates this development and extracts useful information based on packet level characteristics. Distinguishing characteristics from e-mail, HTTP, print, and FTP applications are identified and analyzed. The analysis of collected data from an ad-hoc wireless network reveals that distinguishing characteristics of network traffic do indeed exist. These characteristics include packet size, packet frequency, inter-packet correlation, and channel utilization. Without knowing the contents of packets or the direction of the traffic flow, the applications accessing the wireless network can be determined

Hierachical methods for large population speaker identification using telephone speech

This study focuses on speaker identificat ion. Several problems such as acoustic noise, channel noise, speaker variability, large population of known group of speakers wi thin the system and many others limit good SiD performance. The SiD system extracts speaker specific features from digitised speech signa] for accurate identification. These feature sets are clustered to form the speaker template known as a speaker model. As the number of speakers enrolling into the system gets larger, more models accumulate and the interspeaker confusion results. This study proposes the hierarchical methods which aim to split the large population of enrolled speakers into smaller groups of model databases for minimising interspeaker confusion

Biologically inspired speaker verification

Speaker verification is an active research problem that has been addressed using a variety of different classification techniques. However, in general, methods inspired by the human auditory system tend to show better verification performance than other methods. In this thesis three biologically inspired speaker verification algorithms are presented

A segment-based speaker verification system using SUMMIT

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (p. 75-79).by Sridevi Vedula Sarma.M.S

Application of statistical learning theory to plankton image analysis

Submitted to the Joint Program in Applied Ocean Science and Engineering 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 2006A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed. One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not “good” ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large realworld dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.This work was supported by National Science Foundation Grants OCE-9820099 and Woods Hole Oceanographic Institution academic program

Application of statistical learning theory to plankton image analysis

Thesis (Ph. D.)--Joint Program in Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2006.Includes bibliographical references (leaves 155-173).A fundamental problem in limnology and oceanography is the inability to quickly identify and map distributions of plankton. This thesis addresses the problem by applying statistical machine learning to video images collected by an optical sampler, the Video Plankton Recorder (VPR). The research is focused on development of a real-time automatic plankton recognition system to estimate plankton abundance. The system includes four major components: pattern representation/feature measurement, feature extraction/selection, classification, and abundance estimation. After an extensive study on a traditional learning vector quantization (LVQ) neural network (NN) classifier built on shape-based features and different pattern representation methods, I developed a classification system combined multi-scale cooccurrence matrices feature with support vector machine classifier. This new method outperforms the traditional shape-based-NN classifier method by 12% in classification accuracy. Subsequent plankton abundance estimates are improved in the regions of low relative abundance by more than 50%. Both the NN and SVM classifiers have no rejection metrics. In this thesis, two rejection metrics were developed.(cont.) One was based on the Euclidean distance in the feature space for NN classifier. The other used dual classifier (NN and SVM) voting as output. Using the dual-classification method alone yields almost as good abundance estimation as human labeling on a test-bed of real world data. However, the distance rejection metric for NN classifier might be more useful when the training samples are not "good" ie, representative of the field data. In summary, this thesis advances the current state-of-the-art plankton recognition system by demonstrating multi-scale texture-based features are more suitable for classifying field-collected images. The system was verified on a very large real-world dataset in systematic way for the first time. The accomplishments include developing a multi-scale occurrence matrices and support vector machine system, a dual-classification system, automatic correction in abundance estimation, and ability to get accurate abundance estimation from real-time automatic classification. The methods developed are generic and are likely to work on range of other image classification applications.by Qiao Hu.Ph.D

Impact of access control and copyright in e-learning from user’s perspective in the United Kingdom

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe widespread adoption of E-Learning has largely been driven by the recommendations of educational technologists seeking to convey the benefits of E-Learning as a valuable accessory to teaching and possible solution for distance-based education. Research in the E-Learning domain has mainly focused on providing and delivering content andinfrastructure. Security issues are usually not taken as central concern in most implementations either because systems are usually deployed in controlled environments, or because they take the one-to-one tutoring approach, not requiring strict security measures. The scope of this research work is to investigate the impact of Access Control and Copyright in E-Learning system. An extensive literature review, theories from the field of information systems, psychology and cognitive sciences, distance and online learning, as well as existing E-Learning models show that research in E-learning is still hardly concerned with the issues of security. It is obvious that E-learning receives a new meaning as technology advances and business strategies change. The trends of learning methods have also led to the adjustment of National Curriculum and standards. However, research has also shown that any strategy or development supported by the Internet requires security and is therefore faced with challenges. This thesis is divided into six Chapters. Chapter 1 sets the scene for the research rationale and hypotheses, and identifies the aims and objectives. Chapter 2 presents the theoretical background and literature review. Chapter 3 is an in-depth review of the methods and methodology with clear justification of their adaptation and explains the underlying principles. Chapter 4 is based on the results and limitations obtained from the six case studies observations supported with literature review and ten existing models, while Chapter 5 is focused on the questionnaire survey. Chapter 6 describes the proposed Dynamic E-Learning Access Control and Copyright Framework (DEACCF) and the mapping of the threats from the Central Computing and Telecommunications Agency (CCTA) Risk Analysis and Management Method (CRAMM) to Annualised Loss Expectancy (ALE). Chapter 7 presents the conclusions and recommendations, and the contribution to knowledge with further development plans for future work