@inproceedings{bf301a362a7540ba916d2b0357bbac3e,
title = "A study on underwater target recognition applying auditory slow feature analysis",
abstract = "Human listeners are capable of segregating and recognizing the class of signal better than machine recognizer in complex noisy conditions. In this paper, we proposed a novel approach for underwater target recognition applying auditory slow feature analysis (ASFA) based on gammatone (GT) filter and slow feature analysis. Our experimental evaluations show that the ASFA feature was proved to be considerably better than conventional acoustic features (i.e. Mel-frequency cepstral coefficients, MFCC). Moreover, the proposed ASFA feature is used for underwater target recognition system to yield promising recognition performance.",
keywords = "auditory slow feature analysis, feature extraction, gammatone filter, underwater target recognition",
author = "Yaozhen Wu and Yixin Yang and Can Tao and Pei Li and Long Yang",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; OCEANS 2014 MTS/IEEE Taipei Conference: Oceans Regeneration ; Conference date: 07-04-2014 Through 10-04-2014",
year = "2014",
month = nov,
day = "20",
doi = "10.1109/OCEANS-TAIPEI.2014.6964334",
language = "英语",
series = "OCEANS 2014 - TAIPEI",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "OCEANS 2014 - TAIPEI",
}