Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe PDF

By Raghunath S. Holambe

ISBN-10: 1461415047

ISBN-13: 9781461415046

ISBN-10: 1461415055

ISBN-13: 9781461415053

Advances in Non-Linear Modeling for Speech Processing comprises complex themes in non-linear estimation and modeling options in addition to their functions to speaker attractiveness.

Non-linear aeroacoustic modeling method is used to estimate the real fine-structure speech occasions, which aren't printed by means of the fast time Fourier remodel (STFT). This aeroacostic modeling strategy presents the impetus for the excessive solution Teager power operator (TEO). This operator is characterised via a time solution which may music swift sign power adjustments inside of a glottal cycle.

The cepstral beneficial properties like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the value spectrum of the speech body and the part spectra is ignored. to beat the matter of neglecting the section spectra, the speech construction method could be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and prompt frequency elements, the strength separation set of rules (ESA) and the Hilbert remodel demodulation (HTD) set of rules are mentioned.

Different beneficial properties derived utilizing above non-linear modeling suggestions are used to strengthen a speaker id approach. eventually, it's proven that, the fusion of speech construction and speech belief mechanisms can result in a powerful function set.

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The model discussed by Ghahramani et al. in [21]. The time-invariant (stationary) version of such a simplified model has the state-space form of x(k + 1) = g[x(k), u] + w(k)] o(k) = h[x(k)] + v(k). ) in Eq. 1 which is nonlinear in the previous outputs x[n − k] but linear in the innovation signal e(n) [22, 23]. , x[n − N ]) + e[n]. 27) 38 3 Linear and Dynamic System Model Like the linear model, both the prediction and the synthesis modes can be explored here. For prediction mode, the minimum mean-square error is obtained by the conditional expectation of x[n] given the vector of past samples x[n − 1] [24, 25].

2 FM Signals Just like the AM signals, an FM signal is the combination of two signals, where one is the single frequency sinusoidal signal, the carrier and the other is the baseband signal, however, in FM signals the baseband signal is used to change the frequency of the carrier signal. 7 Common Signals of Interest 23 where A is the signal amplitude, Ωc is the carrier frequency, Ωm is the maximum frequency deviation with Ωm ∈ [0, Ωc ], q(t) is the baseband signal with |q(t)| ≤ 1, and θ is the constant phase offset.

29) For the practical design of a nonlinear predictor based on Eq. 29, various approximations of the conditional expectation operator have been proposed by Casdagli et al. [26] and Farmer et al. [27]. Various parametric approximation methods have been proposed by researchers like, Sicuranza [28], Thyssen et al. [29], Singer et al. [30], Tong [23], Priestley [31] etc. Radial basis function approximations have been proposed by Birgmeier [32, 33] and Diaz et al. [34] whereas Thyssen et al. [29], Lapedes et al.

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Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe

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