Jagathy Raj, V P; Hari, V S; Gopikakumari, R(IEEE, February 10, 2011)
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Abstract:
Modeling nonlinear systems using Volterra series is
a century old method but practical realizations were hampered by
inadequate hardware to handle the increased computational complexity
stemming from its use. But interest is renewed recently,
in designing and implementing filters which can model much
of the polynomial nonlinearities inherent in practical systems.
The key advantage in resorting to Volterra power series for this
purpose is that nonlinear filters so designed can be made to
work in parallel with the existing LTI systems, yielding improved
performance. This paper describes the inclusion of a quadratic
predictor (with nonlinearity order 2) with a linear predictor
in an analog source coding system. Analog coding schemes
generally ignore the source generation mechanisms but focuses
on high fidelity reconstruction at the receiver. The widely used
method of differential pnlse code modulation (DPCM) for speech
transmission uses a linear predictor to estimate the next possible
value of the input speech signal. But this linear system do not
account for the inherent nonlinearities in speech signals arising
out of multiple reflections in the vocal tract. So a quadratic
predictor is designed and implemented in parallel with the linear
predictor to yield improved mean square error performance. The
augmented speech coder is tested on speech signals transmitted
over an additive white gaussian noise (AWGN) channel.
Description:
Communications and Signal Processing (ICCSP), 2011 International Conference on