Recognition of vessel acoustic signatures using non-linear teager energy based features

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buir.contributor.authorÇetin, A. Enis
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage5en_US
dc.citation.spage1en_US
dc.contributor.authorCan, Gökmenen_US
dc.contributor.authorAkbaş, Cem Emreen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.date.accessioned2018-04-12T11:48:58Z
dc.date.available2018-04-12T11:48:58Z
dc.date.issued2016-10en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis paper proposes a vessel recognition and classification system based on vessel acoustic signatures. Teager Energy Operator (TEO) based Mel Frequency Cepstral Coefficients (MFCC) are used for the first time in Underwater Acoustic Signal Recognition (UASR) to identify platforms the acoustic noise they generate. TEO based MFCC (TEO-MFCC), being more robust in noisy conditions than conventional MFCC, provides a better estimation platform energy. Conventionally, acoustic noise is recognized by sonar oper-ators who listen to audio signals received by ship sonars. The aim of this work is to replace this conventional human-based recognition system with a TEO-MFCC features-based classification system. TEO is applied to short-time Fourier transform (STFT) of acoustic signal frames and Mel-scale filter bank is used to obtain Mel Teager-energy spectrum. The feature vector is constructed by discrete cosine transform (DCT) of logarithmic Mel Teager-energy spectrum. Obtained spectrum is transformed into cepstral coefficients that are labeled as TEO-MFCC. This analysis and implementation are carried out with datasets of 24 different noise recordings that belong to 10 separate classes of vessels. These datasets are partially provided by National Park Service (NPS). Artificial Neural Networks (ANN) are used as a classification method. Experimental results demonstrate that TEO-MFCC achieves 99.5% accuracy in classification of vessel noises. © 2016 IEEE.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T11:48:58Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1109/IWCIM.2016.7801190en_US
dc.identifier.urihttp://hdl.handle.net/11693/37718
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IWCIM.2016.7801190en_US
dc.source.titleInternational Workshop on Computational Intelligence for Multimedia Understanding, IWCIM 2016en_US
dc.subjectMFCCen_US
dc.subjectTeager energyen_US
dc.subjectVessel recognitionen_US
dc.subjectAcoustic noiseen_US
dc.subjectAcoustic variables measurementen_US
dc.subjectAcoustic wavesen_US
dc.subjectArtificial intelligenceen_US
dc.subjectAudio acousticsen_US
dc.subjectDiscrete cosine transformsen_US
dc.subjectNeural networksen_US
dc.subjectSonaren_US
dc.subjectSpectroscopyen_US
dc.subjectSpeech recognitionen_US
dc.subjectDiscrete Cosine Transform(DCT)en_US
dc.subjectMel-frequency cepstral coefficientsen_US
dc.subjectMFCCen_US
dc.subjectShort time Fourier transformsen_US
dc.subjectTeager energyen_US
dc.subjectTeager energy operatorsen_US
dc.subjectUnderwater acoustic signalen_US
dc.subjectVessel Recognitionen_US
dc.subjectUnderwater acousticsen_US
dc.titleRecognition of vessel acoustic signatures using non-linear teager energy based featuresen_US
dc.typeConference Paperen_US

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