Bandwidths
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Bandwidth Extension of Speech Using Perceptual Criteria
Bandwidth extension of speech is used in the International Telecommunication Union G.729.1 standard in which the narrowband bitstream is combined with quantized high-band parameters. Although this system produces high-quality wideband speech, the additional bits used to represent the high band can be further reduced. In addition to the algorithm used in the G.729.1 standard, bandwidth extension methods based on spectrum prediction have also been proposed. Although these algorithms do not require additional bits, they perform poorly when the correlation between the low and the high band is weak. In this book, two wideband speech coding algorithms that rely on bandwidth extension are developed. The algorithms operate as wrappers around existing narrowband compression schemes. More specifically, in these algorithms, the low band is encoded using an existing toll-quality narrowband system, whereas the high band is generated using the proposed extension techniques. The first method relies only on transmitted high-band information to generate the wideband speech. The second algorithm uses a constrained minimum mean square error estimator that combines transmitted high-band envelope information with a predictive scheme driven by narrowband features. Both algorithms make use of novel perceptual models based on loudness that determine optimum quantization strategies for wideband recovery and synthesis. Objective and subjective evaluations reveal that the proposed system performs at a lower average bit rate while improving speech quality when compared to other similar algorithms.
Ecology, Engineering, and Management
Author: Michel J. G. van Eeten
language: en
Publisher: Oxford University Press
Release Date: 2002-04-18
Ecology, Engineering, and the Paradox of Management is the first book that addresses and reconciles what many take to be the core paradox facing environmental decision-makers and stakeholders: How do they restore the environment while at the same time provide ever more services reliably from that environment, including clean air, water and energy for more and more people? The book provides a conceptual framework, empirical case analyses, and organizational proposals to resolve the paradox, be it in the US, Europe, or elsewhere. Thus, Ecology, Engineering, and the Paradox of Management has multiple audiences. First are the key professions involved in the protection and improvement of ecosystems and in the provision and delivery of services from those ecosystems. These include ecologists (and other natural scientists such as conservation biologists, climatologists, forest scientists, and toxicologists), engineers (as well as hydrologists, environmental engineers, civil engineers, and line operators), modeling and gaming experts, managers, planners, and power, agriculture, and recreation communities. Another audience includes university researchers in ecology, conservation biology, engineering, the policy sciences, and resource management. Those interested in interdisciplinary approaches in these fields will also find the book especially helpful. Finally, those interested in the Everglades, the Columbia River Basin, San Francisco Bay-Delta, and the Green Heart of western Netherlands will find new insights here, as the book provides a detailed examination of the paradox in each of these cases.
Multivariate Kernel Smoothing and Its Applications
Kernel smoothing has greatly evolved since its inception to become an essential methodology in the data science tool kit for the 21st century. Its widespread adoption is due to its fundamental role for multivariate exploratory data analysis, as well as the crucial role it plays in composite solutions to complex data challenges. Multivariate Kernel Smoothing and Its Applications offers a comprehensive overview of both aspects. It begins with a thorough exposition of the approaches to achieve the two basic goals of estimating probability density functions and their derivatives. The focus then turns to the applications of these approaches to more complex data analysis goals, many with a geometric/topological flavour, such as level set estimation, clustering (unsupervised learning), principal curves, and feature significance. Other topics, while not direct applications of density (derivative) estimation but sharing many commonalities with the previous settings, include classification (supervised learning), nearest neighbour estimation, and deconvolution for data observed with error. For a data scientist, each chapter contains illustrative Open data examples that are analysed by the most appropriate kernel smoothing method. The emphasis is always placed on an intuitive understanding of the data provided by the accompanying statistical visualisations. For a reader wishing to investigate further the details of their underlying statistical reasoning, a graduated exposition to a unified theoretical framework is provided. The algorithms for efficient software implementation are also discussed. José E. Chacón is an associate professor at the Department of Mathematics of the Universidad de Extremadura in Spain. Tarn Duong is a Senior Data Scientist for a start-up which provides short distance carpooling services in France. Both authors have made important contributions to kernel smoothing research over the last couple of decades.