Time series signal recognition with signal variation proof learning

Abstract

Time series signals are recognized by extracting a multiplicity of candidate feature vectors characterizing an individual time series signal without fixing a boundary for the individual time series signal, and calculating similarity values for each of the multiplicity of candidate feature vectors and the reference patterns stored in the recognition dictionary, from which one reference pattern for which the similarity value is greater than a prescribed threshold value is selected as a recognition result. New reference patterns to be stored in the recognition dictionary are learned by artificially synthesizing signal patterns with variations for learning; extracting feature vectors for learning from the recognition results and the similarity values obtained by the recognizing step from the signal patterns with variations for learning; and obtaining new reference patterns from the feature vectors for learning extracted by the extracting step.