Discrete cosine transform based de-noising of glottal pulses

Reliable estimates of the glottal function are of major
importance in speech/voice processing for the characterization of voicing
conditions, description of various phonation types, and identification of their
parameters. This paper presents a new method for de-noising glottal wavelets
(Differentiated Glottal Volume Velocity Pulses) and separation of the noise
component, based on an approximation of their Discrete Cosine Transform as a sum
of Exponentially Damped Sinusoids. The identification of the Exponentials'
parameters leads to convenient estimation of ''clean'' glottal wavelets and thus
separation of noise disturbances. The method is compared to standard Low-pass
filtering and Wavelet de-noising using Monte Carlo simulations on synthetic
Liljencrants-Fant glottal pulse models. As shown, the method supercedes for
lower SNRs. Moreover, the method does not require exact determination of control
parameters thus offering ease of implementation.