000 04219nam a22004815i 4500
001 978-3-031-01516-8
003 DE-He213
005 20240730164326.0
007 cr nn 008mamaa
008 220601s2011 sz | s |||| 0|eng d
020 _a9783031015168
_9978-3-031-01516-8
024 7 _a10.1007/978-3-031-01516-8
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC067000
_2bisacsh
072 7 _aTJF
_2thema
072 7 _aUYS
_2thema
082 0 4 _a621,382
_223
100 1 _aAtti, Venkatraman.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983907
245 1 0 _aAlgorithms and Software for Predictive and Perceptual Modeling of Speech
_h[electronic resource] /
_cby Venkatraman Atti.
250 _a1st ed. 2011.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2011.
300 _aIX, 113 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 canbe 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.
650 0 _aSignal processing.
_94052
650 1 4 _aSignal, Speech and Image Processing.
_931566
710 2 _aSpringerLink (Online service)
_983909
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031003882
776 0 8 _iPrinted edition:
_z9783031026447
830 0 _aSynthesis Lectures on Algorithms and Software in Engineering,
_x1938-1735
_983910
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01516-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85580
_d85580