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A Novel Modeling and Recognition Method for Underwater Sound Based on HMT in Wavelet Domain

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Book cover AI 2004: Advances in Artificial Intelligence (AI 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3339))

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Abstract

To modeling and classify underwater sound, hidden Markov tree (HMT) model in wavelet domain is adopted. Taking advantage of the models, the simulation time sequence of ocean noise can be produced. An improved classification approach based on HMT model and fuzzy maximum and minimum neural net work (FMMNN) is brought forward, which integrates the wavelet coefficients HMT models with FMMNN. The performance of this approach is evaluated experimentally in classifying four types of ocean noises. With an accuracy of more than 86%, this HMT-based approach is found to outperform previously proposed classifiers. Experiments prove that the new method is effective.

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References

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Author information

Authors and Affiliations

  1. Institute of Image Processing & Pattern Recognition, SJTU, 200030, China

    Zhou Yue, Kong Wei & Xu Qing

Authors
  1. Zhou Yue
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  2. Kong Wei
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  3. Xu Qing
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Editor information

Editors and Affiliations

  1. Faculty of Information Technology, Monash University, VIC 3800, Australia

    Geoffrey I. Webb

  2. Science, Engineering and Technology Portfolio, Royal Melbourne Institute of Technology, VIC 3001, Melbourne, Australia

    Xinghuo Yu

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© 2004 Springer-Verlag Berlin Heidelberg

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Yue, Z., Wei, K., Qing, X. (2004). A Novel Modeling and Recognition Method for Underwater Sound Based on HMT in Wavelet Domain. In: Webb, G.I., Yu, X. (eds) AI 2004: Advances in Artificial Intelligence. AI 2004. Lecture Notes in Computer Science(), vol 3339. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30549-1_30

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  • DOI: https://doi.org/10.1007/978-3-540-30549-1_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-24059-4

  • Online ISBN: 978-3-540-30549-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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