Language Identification Using Spectral and Prosodic Features, K. Sreenivasa Rao; V. Ramu Reddy; Sudhamay Maity
Автор: K. Sreenivasa Rao; Shashidhar G. Koolagudi Название: Robust Emotion Recognition using Spectral and Prosodic Features ISBN: 1461463599 ISBN-13(EAN): 9781461463597 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this brief, the authors discuss recently explored spectral (sub-segmental and pitch synchronous) and prosodic (global and local features at word and syllable levels in different parts of the utterance) features for discerning emotions in a robust manner.
Автор: K. Sreenivasa Rao; Dipanjan Nandi Название: Language Identification Using Excitation Source Features ISBN: 3319177249 ISBN-13(EAN): 9783319177243 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book discusses how in implicit processing approach, excitation source features are derived from LP residual, Hilbert envelope (magnitude) of LP residual and Phase of LP residual;
Автор: Ruckebusch, Cyril Название: Resolving Spectral Mixtures,30 ISBN: 0444636382 ISBN-13(EAN): 9780444636386 Издательство: Elsevier Science Рейтинг: Цена: 23244.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem--from multivariate curve resolution and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions from individual unknown components with limited prior information is a complex problem that has attracted continuous interest for almost four decades.
Spectral unmixing is a topic of interest in statistics, chemometrics, signal processing, and image analysis. For decades, researchers from these fields were often unaware of the work in other disciplines due to their different scientific and technical backgrounds and interest in different objects or samples. This led to the development of quite different approaches to solving the same problem. This multi-authored book will bridge the gap between disciplines with contributions from a number of well-known and strongly active chemometric and signal processing research groups.
Among chemists, multivariate curve resolution methods are preferred to extract information about the nature, amount, and location in time (process) and space (imaging and microscopy) of chemical constituents in complex samples. In signal processing, assumptions are usually around statistical independence of the extracted components. However, the chapters include the complexity of the spectral data to be unmixed as well as dimensionality and size of the data sets. Advanced spectroscopy is the key thread linking the different chapters. Applications cover a large part of the electromagnetic spectrum. Time-resolution ranges from femtosecond to second in process spectroscopy and spatial resolution covers the submicronic to macroscopic scale in hyperspectral imaging.
Описание: This book covers the basics of processing and spectral analysis of monovariate discrete-time signals. Prior advanced mathematical skills are not needed in order to understand the contents: a good command of basic mathematical analysis is sufficient.
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