Speech modelling by non-stationary partials with time varying amplitude and frequency

We have developed a new method for speech decomposition and modeling. The purpose of this approach is to obtain better performance for modeling speech signals corrupted by non-stationary noise. The signal is first divided into frames and then each frame is decomposed into chirp-like partials which are linearly modulated in both amplitude and frequency. The proposed approach, based on the complex ambiguity function (CAF), can successfully estimate the parameters of each partial without assuming the harmonic structure of the signal. The effectiveness of this approach shows its potential for use in a speech enhancement system.