By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)

ISBN-10: 3540773460

ISBN-13: 9783540773467

This quantity comprises the court cases of NOLISP 2009, an ISCA educational and Workshop on Non-Linear Speech Processing held on the college of Vic (- talonia, Spain) in the course of June 25-27, 2009. NOLISP2009wasprecededbythreeeditionsofthisbiannualeventheld2003 in Le Croisic (France), 2005 in Barcelona, and 2007 in Paris. the most notion of NOLISP workshops is to offer and talk about new principles, concepts and effects concerning replacement methods in speech processing which can leave from the mainstream. so that it will paintings on the front-end of the topic sector, the next domain names of curiosity were de?ned for NOLISP 2009: 1. Non-linear approximation and estimation 2. Non-linear oscillators and predictors three. Higher-order information four. self sustaining part research five. Nearest acquaintances 6. Neural networks 7. determination timber eight. Non-parametric versions nine. Dynamics for non-linear structures 10. Fractal tools eleven. Chaos modeling 12. Non-linear di?erential equations The initiative to prepare NOLISP 2009 on the collage of Vic (UVic) got here from the UVic study workforce on sign Processing and used to be supported through the Hardware-Software examine staff. we want to recognize the ?nancial help bought from the M- istry of technological know-how and Innovation of Spain (MICINN), collage of Vic, ISCA, and EURASIP. All contributions to this quantity are unique. They have been topic to a doub- blind refereeing technique sooner than their reputation for the workshop and have been revised after being awarded at NOLISP 2009.

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A11 state 1 p1(o1) o1 a22 a12 state 2 p2(o2) o2 a33 a23 state 3 p3(o3) o3 Fig. 6. Example of a HMM model with three states estimate the associated states sequence thanks to the Viterbi algorithm [21]. All these probabilies are thus required to compute the sequence of hidden states. The very first stage is then to estimate them using the Baum-Welch algorithm over a training set. 3 HMMs Extensions Two other kinds of statistical models may be derived from the classical HMMs to facilitate audiovisual process modeling, namely the Multistream HMMs and the coupled HMMs (CHMMs) [22,23,24].

Fig. 3. Face with maximum r (left), selected (center) and rejected face (right) (8 bins). Each keypoint is also defined regarding three other data: its spatial location, its scale and its orientation. 3). Global features. Local methods require a precise localization of particular points within the region of interest. Depending on illumination, occlusions, such a localization may not be easily obtained. Global methods then allow to overcome this drawback. 2). Their extraction is illustrated in figure 4.

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Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers by Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)


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