By Bertrand Rivet, Jonathon Chambers (auth.), Jordi Solé-Casals, Vladimir Zaiats (eds.)
This quantity includes the complaints of NOLISP 2009, an ISCA educational and Workshop on Non-Linear Speech Processing held on the collage 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 proposal of NOLISP workshops is to give and speak about new principles, recommendations and effects with regards to replacement techniques in speech processing which could leave from the mainstream. with a view to paintings on the front-end of the topic sector, the subsequent 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 statistics four. autonomous part research five. Nearest acquaintances 6. Neural networks 7. choice bushes eight. Non-parametric versions nine. Dynamics for non-linear structures 10. Fractal equipment eleven. Chaos modeling 12. Non-linear di?erential equations The initiative to arrange NOLISP 2009 on the college of Vic (UVic) got here from the UVic study team on sign Processing and was once supported by way of the Hardware-Software study workforce. we want to recognize the ?nancial aid received from the M- istry of technology and Innovation of Spain (MICINN), college of Vic, ISCA, and EURASIP. All contributions to this quantity are unique. They have been topic to a doub- blind refereeing method sooner than their popularity for the workshop and have been revised after being awarded at NOLISP 2009.
Read Online or Download Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers PDF
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Additional info for Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27. Revised Selected Papers
A stage of learning is thus required to produce this ”strong” classiﬁer. The nature of the data included in the learning base then inﬂuences the type of faces which can be correctly detected afterwards. As a consequence, diﬀerent cascades must be learnt Some Experiments in Audio-Visual Speech Processing 31 to allow the detection of faces under diﬀerent orientations. Typically, a cascade is dedicated to frontal faces detection and another one to proﬁle faces detection. 2). Faces are ﬁrst extracted thanks to a frontal cascade.
In order to obtain audio and visual features synchronicity, a simple element-wise 42 G. Chollet et al. 1 ... 64 . 1 DCT Extraction . Extract . 100 most Energetic . 64 + 100 1 1 1 Mean . . LDA . 100 40 1 Upsampling with Linear . . 1 . . . 40 LDA . . - . 40 40 40 15 Frames 600 "DCT" E[ . ] 1 ... 200 1 . . 4096 . Feature Normalization . 1 1 t 1 1 "DCT2" Rate = 100Hz Interpolation 200 Video Signal Rate = 25Hz "PRO" 1 t X-Profile 115 110 105 0 20 40 60 80 100 120 140 160 180 1 . 200 . 150 140 130 Y-Profile 120 110 100 .
For instance, Gaussian Mixture Models model the distribution of observation vectors as a combination of gaussian distributions. 3). Hidden Markov Models (HMMs) then allow to model a statistical process involving diﬀerent states. These models are at the heart of many audiovisual applications and are detailed in this section. 1 Gaussian Mixture Models As already mentionned, GMM distribution is a mixture whose components are classical Gaussian distributions. e. its probability of appearance), the mean vector and the covariance matrix of this k th Gaussian component.