Gaussian mixture linear prediction

This work introduces an approach to linear predictive signal analysis utilizing a Gaussian mixture autoregressive model. By initializing different autoregressive states of the model to approximately correspond to the target signal and the expected type of undesired signal components, such as background noise, the iterative parameter estimation converges towards a focused linear prediction model of the target signal. Differently initialized and trained variants of mixture linear prediction are evaluated using objective spectrum distortion measures as well as in feature extraction for speech detection in the presence of ambient noise. In these evaluations, the novel analysis methods perform better than the Fourier transform and conventional linear prediction.