Underwater acoustic target classification and auditory feature identification based on dissimilarity evaluation | Zendy

Yang Li-Xue | Zendy; Kean Chen | Zendy; Bingrui Zhang | Zendy; Liang Yong | Zendy

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Underwater acoustic target classification and auditory feature identification based on dissimilarity evaluation

Author(s) -

Yang Li-Xue,

Kean Chen,

Bingrui Zhang,

Liang Yong

Publication year - 2014

Publication title -

acta physica sinica

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.199

H-Index - 47

ISSN - 1000-3290

DOI - 10.7498/aps.63.134304

Subject(s) - computer science , perception , construct (python library) , identification (biology) , underwater , pattern recognition (psychology) , speech recognition , feature vector , filter bank , artificial intelligence , filter (signal processing) , psychology , computer vision , oceanography , botany , biology , geology , neuroscience , programming language

The purpose of this study is to explore perceptual classification of underwater acoustic targets and auditory features used by human being. First, we design a paired comparison experiment. Then we use the CLASCAL algorithm to model the dissimilarity ratings as a perceptual space, and analyze the properties in three common dimensions, specialties, 3 subjects' latent classes and their roles in target perceptual classification. Finally, based on the gammatone filterbank, we find some auditory features that can effectively underlie 3 common dimensions and beat properties, so as to use them to construct a binary decision tree to classify some new samples; thus we can provide some guidance about how to use these features in practical applications.

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