Recent Developments in Nonlinear Partial Differential by Donatella Danielli (ed.)

By Donatella Danielli (ed.)

This quantity comprises examine and expository articles in line with talks offered on the second Symposium on research and PDEs, held at Purdue collage. The Symposium interested in subject matters on the topic of the speculation and purposes of nonlinear partial differential equations which are on the vanguard of present overseas examine. Papers during this quantity supply a finished account of the various fresh advancements within the box. the themes featured during this quantity comprise: kinetic formulations of nonlinear PDEs; contemporary exact continuation effects and their functions; concentrations and restricted Hamilton-Jacobi equations; nonlinear Schrodinger equations; quasiminimal units for Hausdorff measures; Schrodinger flows into Kahler manifolds; and parabolic predicament issues of purposes to finance. The transparent and concise presentation in lots of articles makes this quantity appropriate for either researchers and graduate scholars

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These filters are triangular and cover the 156 6844 Hz frequency range; they are spaced on the Mel-frequency scale. This scale is logarithmic above 1 kHz and linear below this frequency. These filters are applied to the log of the magnitude spectrum of the signal, which is estimated on a short-time basis. 7) where N is the number of the cepstral coefficients, M is the analysis order and Xm ; m D 1; 2; :::; M , represents the log-energy output of the mt h filter. 20 triangular bandpass filters were used.

1007/978-1-4419-9685-5 6, © Springer Science+Business Media, LLC 2011 43 44 6 Robustness of Automatic Speech Recognition algorithm implemented in these architectures is based on Hidden Markov Models (HMMs), which belong to the class of statistical methods [73]. Other approaches have been developed, but due to the complexity of their usability, they are still considered as research and development tools. Among these techniques, we can cite the one using hybrid neural networks and HMMs [15]. Speech recognition technology is already capable of delivering a good performance for many practical applications.

Given the fact k that the magnitudes of the low-order eigenvalues are higher than the magnitudes of the high-order ones, the effect of the noise on the low-order eigenvalues is less than that of high-order ones. Thus, the variations of Q for a certain noise variance n2 and a certain value of r tend to zero for higher order eigenvalues. 20. 6 Two-Stage Noise Removal Technique The two-stage noise removal algorithm consists of two levels of MFCC enhancement. 1, at the first level, the noisy 13-dimensional vector (12 MFCCs + energy) is fed to a Multi-Layer Perceptron in order to reduce the noise effects on such a vector.

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