In this work a glottal model loosely based on the Ishizaka and Flanagan model is proposed, where the number of parameters is drastically reduced. First, the glottal excitation waveform is estimated, together with the vocal tract filter parameters, using inverse filtering techniques. Then the estimated waveform is used in order to identify the nonlinear glottal model, represented by a closedloop configuration of two blocks: a second order resonant filter, tuned with respect to the signal pitch, and a regressor-based functional, whose coefficients are estimated via nonlinear identification techniques. The results show that an accurate identification of real data can be achieved with less than regressors of the nonlinear functional, and that an intuitive control of fundamental features, such as pitch and intensity, is allowed by acting on the physically informed parameters of the model. 10

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In this work we present an extension of the classic schema of a time-varying filter excited with white noise for the modeling of noise signals from musical instrument sounds. The framework used is that of statistical signal processing, and a structure that combines an Autoregressive (AR) model with an adaptive FIR filter is proposed. A combbased structure for the AR filter is used when tuned noise is to be modeled. The analysis/resynthesis schema proposed is used to perform some basic sound transformations such as time stretching, tuning and energy envelop control, and spectral processing.

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