Mathematical Modeling and Signal Processing in Speech and by Jack Xin, Yingyong Qi

By Jack Xin, Yingyong Qi

The purpose of the booklet is to offer an obtainable creation of mathematical types and sign processing tools in speech and listening to sciences for senior undergraduate and starting graduate scholars with simple wisdom of linear algebra, differential equations, numerical research, and likelihood. Speech and listening to sciences are primary to varied technological advances of the electronic global long ago decade, from track compression in MP3 to electronic listening to aids, from community dependent voice enabled companies to speech interplay with cellphones. arithmetic and computation are in detail relating to those leaps and limits. nevertheless, speech and listening to are strongly interdisciplinary components the place numerous clinical and engineering guides and ways usually coexist and make it tough for newbies to go into.

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Here k must be small enough to ensure stability of the difference scheme and to keep the computed flow velocity positive (no back flow is allowed). 2); where we apply central differencing in space and backward differencing in time. When VFs approach closure, the ODE's in step two become rather stiff, which calls for implicit backward differencing in time [64]. 003165); time step k::; 10-7 . The time unit is ms, length unit cm, speed of sound a = 35 em / ms. 1. A convergence test of numerics is shown in the plot of the air volume velocity passing through the glottis as a function of time in Fig.

Is called a discrete stochastic process, with joint distribution FX;I'x;2 ,... Xii' ij = 1,2, ... as its probability law. A Gaussian process results if all joint distributions of any finitely many random variables are Gaussian. A continuous stochastic process X(t) = X(t, (0), t E [0,1], is a function of two variables, or a mapping X: [0, 1] x Q ---+ JR, where X is a r. v. for each t, and for each (0, we have a sample path (a realization) or trajectory of the process. The associated first and second order statistical quantities are: mean value /1(t) = Var(X(t)), and the covariance function: = E(X(t)), variance (j2(t) C(s,t) = E((X(s) - /1 (s))(X(t) - /1(t))), forsyft.

6556z- 3 . 6556]), then: plot( w, abs(h)), to plot amplitude response (Fig. 5), and plot( w, angl e( h) ) to plot the phase response. 59) 26 I Background Signal Processing, Statistical and Optimization Methods Let us explain Fig. 5 by a zero-pole analysis. 867 j. 2; figure; zplane(zer,pol). The two peaks in Fig. 867 j. The first pole being real (phase angle = 0) determines the location of the first peak at e = o. 2 (at angle n) causes the attenuation at high frequency (the dipping of the curve in near 3).

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