Abstract not available

This thesis addresses the problem of classifying an audio stream as either speech or music, an issue which is beginning to receive increasing attention due to its wide range of applications. Various techniques have been presented in last decade to discriminate between speech and music. However, their accuracy is still not sufficient since music can refer to a very broad class of signals due to the large number of musical instruments found in audio data. Performance can also be further compromised in noisy conditions, which are unavoidable in some practical situations. This thesis presents an analysis of feature extraction techniques and classifiers currently being used, followed by the proposal and evaluation of new features for improved classification. These include two novel cepstral features, delta cepstral energy and power spectrum deviation, along with amplitude and frequency modulation features. The modified group delay feature, initially proposed for speech recognition, is also investigated for speech and music discrimination. Experiments were performed using different sets of features, compared among themselves and with conventional MFCCs using error rate criteria and Detection Error Trade-off curves. It is shown that the proposed cepstral and modulation features result in an increase in the accuracy of the conventional MFCC based system. However, the modified group delay feature which has been shown to improve accuracy for speech classification problems, does not contribute much to the problem of speech and music discrimination. Among the ones presented here the optimum feature configuration, both modulation features with MFCC, resulted in overall error rate of 6.57% as compared to 7.43% for MFCC alone.

The rapid ageing of Japan’s population is occurring in the midst of accelerating social change, causing a rethinking about what it means to grow old. Some older Japanese are pioneering new models for ageing through their involvement in groups known as time banks. These are non-profit organisations which trade time, a universal possession, rather than conventional currency for services. Time given in volunteering is banked for future redemption as assistance for the giver, with points paid per hour. This first study of the impact of time banks on the lives of older Japanese members aims to chart how such groups can help both their senior members and society as a whole.¶ Time banks now exist across the globe but the world’s first time bank was established in 1973 by a Japanese woman. She aspired to create a new form of currency that could give people greater control of their lives and foster warmer community links. The benefits that older time bank members derive include formation of new friendship networks to replace those lost by retirement and the chance to use old skills and learn new ones. Time banks can generate a new form of social capital that fosters traditional Japanese reciprocity and has ikigai or ‘sense of meaning in life’ as one of its main pillars.¶ This research is based on both three-months of fieldwork in Japan and an extensive literature review in Japanese, English and Chinese. It has been by aided by accessibility to the thoughts of the founders of four major time banks through their books and also by their group web sites. My study follows on from an MPhil thesis that compared ageing in China and Japan and draws on my experience living in Japan for 10 years between 1979 and 1991 in both Kansai and Kanto.¶ The literature indicates that social participation is a crucial component for maintaining both psychological and physical health in the later years. While this is a qualitative study and there is yet to be a qualitative review of the effects of time banks in Japan, feedback I received from older members of the first time bank shows that time banks can foster a meaningful later life. ¶ The 21st century has been dubbed that of the aged as greater mass longevity boosts their numbers to unprecedented levels. This thesis questions whether organizations such as time banks can make a significant difference to the quality of life that older people enjoy in this new era. The theoretical framework examines whether the social exchange that these groups nurture can enhance the social capital of their communities, creating a positive image for ageing.

For the prediction of chaotic time series, a dichotomy has arisen between local approaches and global approaches. Local approaches hold the reputation of simplicity and feasibility, but they generally do not produce a compact description of the underlying system and are computationally intensive. Global approaches have the advantage of requiring less computation and are able to yield a global representation of the studied time series. However, due to the complexity of the time series process, it is often not easy to construct a global model to perform the prediction precisely. In addition to these approaches, a combination of the global and local techniques, called mixture of experts (ME), is also possible, where a smaller number of models work cooperatively to implement the prediction. This thesis reports on research about ME models for chaotic time series prediction. Based on a review of the techniques in time series prediction, a HMM-based ME model called "Time-line" Hidden Markov Experts (THME) is developed, where the trajectory of the time series is divided into some regimes in the state space and regression models called local experts are applied to learn the mapping on the regimes separately. The dynamics for the expert combination is a HMM, however, the transition probabilities are designed to be time-varying and conditional on the "real time" information of the time series. For the learning of the "time-line" HMM, a modified Baum-Welch algorithm is developed and the convergence of the algorithm is proved. Different versions of the model, based on MLP, RBF and SVM experts, are constructed and applied to a number of chaotic time series on both one-step-ahead and multi-step-ahead predictions. Experiments show that in general THME achieves better generalization performance than the corresponding single models in one-step-ahead prediction and comparable to some published benchmarks in multi-step-ahead prediction. Various properties of THME, such as the feature selection for trajectory dividing, the clustering techniques for regime extraction, the "time-line" HMM for expert combination and the performance of the model when it has different number of experts, are investigated. A number of interesting future directions for this work are suggested, which include the feature selection for regime extraction, the model selection for transition probability modelling, the extension to distribution prediction and the application on other time series.

Thesis (M.A.R.)--Emmanuel School of Religion, Johnson City, Tennessee, 2004. / Includes bibliographical references (leaves 55-58) and vita.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

<p>The purpose of the essay is to illustrate the differences in understanding and comprehension of time among the characters in the novel Hard Times. These contrasting differences are then argued to compose a crtisism of the industrial society.</p>

Thesis (Ph. D.)--Ohio State University, 1982. / Includes vita. Includes bibliographical references (leaves 125-133). Available online via OhioLINK's ETD Center.

Thesis--Indiana University, 2006. / Computer printout. Advisor: Lewis Rowell. Includes bibliographical references (leaves 233-240), abstract, and vita.

Thesis (M.Sc.)--York University, 2003. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 115-117). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL:http://gateway.proquest.com/openurl?url%5Fver=Z39.88-2004&res%5Fdat=xri:pqdiss&rft%5Fval%5Ffmt=info:ofi/fmt:kev:mtx:dissertation&rft%5Fdat=xri:pqdiss:MQ99408