Open AccessRegression model of timbre attribute for underwater noise and its application to target recognition
Author(s) -
Na Wang,
Kean Chen
Publication year - 2010
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.59.2873
Subject(s) - timbre , computer science , noise (video) , principal component analysis , pattern recognition (psychology) , feature selection , regression analysis , speech recognition , artificial intelligence , set (abstract data type) , underwater , regression , feature (linguistics) , statistics , machine learning , mathematics , art , musical , linguistics , image (mathematics) , philosophy , visual arts , programming language , oceanography , geology
Timbre attribute is the most important feature to recognize a target. This paper presents a model of timbre features by multiple regression analysis applied in the recognition of underwater noise. At first, timbre attribute as a dependent variable is analyzed by the semantic differential evaluation and principal component analysis. And then an extended stepwise variables selection is proposed to select the optimal set as independent variables from auditory features that have been discussed in previous researches. Finally, the timbre features extracted by the regression model are used to recognize the underwater target. The results show that the extended regression analysis as a statistical method can find the relationship between timbre attribute and the auditory features. And the modeling timbre features calculated by several statistics of the sub-spectral features and the sub-temporal features are more effective than other features.