UNSUPERVISED ADAPTATION AND CLASSIFICATION OF MULTI-SOURCE DATA USING A GENERALIZED GAUSSIAN MIXTURE MODEL

摘要

A computer-implemented method and apparatus that adapts class parameters, classifies data and separates sources configured in one of multiple classes whose parameters are initially unknown. The data set may be generated in a dynamic environment where the sources (100) provide signals are mixed and received by sensors (120), and the mixing parameters change without notice and in an unknown manner. A generalized Gaussian mixture model is used to classify the observed data into two or more mutually exclusive classes whose basis functions may be defined by a range of probability density functions. The class parameters for each of the classes are adapted to a data set in an adaptation algorithm in which class parameters including mixing matrices, bias vectors, and pdf parameters are adapted. Each data vector is assigned to one of the learned mutually exclusive classes. In some embodiments the class parameters may have been previously learned, and the system is used to classify the data and if desired to separate the sources. The adaptation and classification algorithms can be utilized in a wide variety of applications such as speech processing, image processing, medical data processing, satellite data processing, antenna array reception, and information retrieval systems.