By Jordi Sole-Casals, Vladimir Zaiats
This e-book constitutes the lawsuits of the overseas convention on Advances in Nonlinear Speech Processing, NOLISP 2009, held in Vic (Barcelona), Spain in the course of June 25-27, 2009. the amount offers with new principles, suggestions and effects concerning substitute techniques in speech processing.
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Extra info for Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27, 2009, Revised Selected
PCA gaussianization for image processing. In: Proc. IEEE ICIP 2009, pp. : Characterization of HVS threshold performance by a weighting function in the Gabor domain. J. Mod. Opt. : Non-linear image representation for eﬃcient perceptual coding. : V1 non-linear properties emerge from local-to-global non-linear ICA. : Color detection and color contrast discrimination thresholds. In: Proc. OSA Meeting, p. : The CSF of human colour vision to red-green and yellowblue chromatic gratings. J. Physiol. : Emergence of simple-cell receptive ﬁeld properties by learning a sparse code for natural images.
In particular, we have shown that a Blackman window centered on the Glottal Closure Instant (GCI) and whose length is twice the pitch period is appropriate in order to achieve a good decomposition. 3 Chirp Decomposition of Speech The Chirp Z-Transform (CZT), as introduced by Rabiner et al  in 1969, allows the evaluation of the Z-transform on a spiral contour in the Z-plane. Its ﬁrst application aimed at separating too close formants by reducing their bandwidth. Nowadays CZT reaches several ﬁelds of Signal Processing such as time interpolation, homomorphic ﬁltering, pole enhancement, narrow-band analysis,...
22 J. Malo and V. 5 γ=1 γ=2 6 5 i p(r ) 4 3 2 1 0 −2 −1 0 r 1 2 i Fig. 3. Family of marginal PDFs of the normalized coeﬃcients ri as a function of γ Even though factorization of the PDF does not depend on γ, it determines the shape of the marginal PDFs (see Fig. 3). However, note that diﬀerent values of γ would imply a better (or worse) match between the denominator of the normalization and the covariance of the image model. e. the validity of Eq. 3) by (1) Mutual Information (MI) measures, and (2) by analyzing the conditional probabilities of the transformed coeﬃcients.
Advances in Nonlinear Speech Processing: International Conference on Nonlinear Speech Processing, NOLISP 2009, Vic, Spain, June 25-27, 2009, Revised Selected by Jordi Sole-Casals, Vladimir Zaiats