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008			230204s2011 sz | s |||| 0|eng d
020			_a9783031015168
024	7		_a10.1007/978-3-031-01516-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK7882.S65 _b2011 EB
100	1		_aAtti, Venkatraman, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686556 _d1978-
245	1	0	_aAlgorithms and Software for Predictive and Perceptual Modeling of Speech _cby Venkatraman Atti
250			_a1st edition 2011
264		1	_aCham _bSpringer International Publishing _c2011
300			_a1 recurso en línea (IX, 113 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Algorithms and Software in Engineering _x1938-1735
505	0		_aIntroduction -- Predictive Modeling of Speech -- Perceptual Modeling of Speech.
520			_aFrom the early pulse code modulation-based coders to some of the recent multi-rate wideband speech coding standards, the area of speech coding made several significant strides with an objective to attain high quality of speech at the lowest possible bit rate. This book presents some of the recent advances in linear prediction (LP)-based speech analysis that employ perceptual models for narrow- and wide-band speech coding. The LP analysis-synthesis framework has been successful for speech coding because it fits well the source-system paradigm for speech synthesis. Limitations associated with the conventional LP have been studied extensively, and several extensions to LP-based analysis-synthesis have been proposed, e.g., the discrete all-pole modeling, the perceptual LP, the warped LP, the LP with modified filter structures, the IIR-based pure LP, all-pole modeling using the weighted-sum of LSP polynomials, the LP for low frequency emphasis, and the cascade-form LP. These extensions can be classified as algorithms that either attempt to improve the LP spectral envelope fitting performance or embed perceptual models in the LP. The first half of the book reviews some of the recent developments in predictive modeling of speech with the help of Matlab™ Simulation examples. Advantages of integrating perceptual models in low bit rate speech coding depend on the accuracy of these models to mimic the human performance and, more importantly, on the achievable "coding gains" and "computational overhead" associated with these physiological models. Methods that exploit the masking properties of the human ear in speech coding standards, even today, are largely based on concepts introduced by Schroeder and Atal in 1979. For example, a simple approach employed in speech coding standards is to use a perceptual weighting filter to shape the quantization noise according to the masking properties of the human ear. The second half of the book reviews some of the recent developments in perceptual modeling of speech (e.g., masking threshold, psychoacoustic models, auditory excitation pattern, and loudness) with the help of Matlab™ simulations. Supplementary material including Matlab™ programs and simulation examples presented in this book can also be accessed here. Table of Contents: Introduction / Predictive Modeling of Speech / Perceptual Modeling of Speech.
988			_aSynthesis Collection of Technology_2011
650		7	_2embne _9147323 _aReconocimiento automático del lenguaje
650		7	_2embne _9670862 _aPredicción, Teoría de la
650		7	_2embne _9140167 _aLenguaje _xReconocimiento automático
776	0	8	_iPrinted edition: _z9783031003882
776	0	8	_iPrinted edition: _z9783031026447
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01516-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _eIG _zSI