Class-specific model mixtures for the classification of acoustic time series

Abstract

We present a new classifier for acoustic time series that involves a mixture of generative models. The models use a variety of segmentation sizes and feature extraction methods, yet can be combined at a higher level using a mixture probability density function (PDF) thanks to the PDF projection theorem (PPT) that converts the feature PDF to raw time series PDFs. The effectiveness of the method is compared with the leading methods and is shown to be superior using three data sets.