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- W1969428142 abstract "New features and algorithms are derived from human perception to improve the automation of active sonar signal classification. Data from human subject research on the perceived similarity of sound pairs can be assembled in the form of a dissimilarity matrix. A dissimilarity matrix can be analyzed in many ways, two of which are multidimensional scaling (MDS) and additive clustering (AC). Different underlying perception models can be inferred from each analysis technique: a feature vector space for MDS and a contrast model for AC. The relevance of each technique to the dissimilarity data is inferred from the quantification of model complexity to the measure of fitting the original data [D. J. Navarro and M. D. Lee, “Common and distinctive features in stimulus similarity,” Psychon. Bull. Rev. 11, pp. 961–974 (2004)]. An experimental dissimilarity matrix is shown and analyzed using MDS and AC. Potential features for each case are shown from singular value decomposition of various signal representations. Signal processing implications of real valued features in the contrast model and alternative implementation are discussed. [The authors gratefully acknowledge conversations with and results from Dr. Jason Summers. Work supported by the Office of Naval Research.]" @default.
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- W1969428142 date "2008-10-01" @default.
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- W1969428142 title "Contrast models and classification of active sonar signals." @default.
- W1969428142 doi "https://doi.org/10.1121/1.4783261" @default.
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