Fusion architectures for the classification of time series

Abstract

The classification of time series based on local features is discussed in this paper. In this context we discuss the topics data fusion, decision fusion, and temporal fusion. Three different classifier architectures for these fusion tasks are proposed. Some local features are automatically derived form the time and frequency domain and categorized through a fuzzy-k-nearest-neighbour rule. Soft decisions are combined to a crisp decision of the whole time series. Numerical results for all architectures are given for a data set (songs of crickets, recorded in Thailand and Ecuador) containing 35 different categories.