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Journal of the Acoustical Society of America / Volume 121 / Issue 4 / ACOUSTIC SIGNAL PROCESSING [60]

Multiple angle acoustic classification of zooplankton

J. Acoust. Soc. Am. Volume 121, Issue 4, pp. 2060-2070 (2007); (11 pages)

Paul L. D. Roberts and Jules S. Jaffe

Marine Physical Laboratory, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238

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The use of multiple angle acoustic scatter to discriminate between two taxa of fluid-like zooplankton, copepods and euphausiids, is explored. Using computer modeling, feature extraction, and subsequent classification, the accuracy in discriminating between the two taxa is characterized via computer simulations. The model applies the distorted wave Born approximation together with a simple system geometry, a linear array, to predict a set of noisy training and test data. Three feature spaces are designed, exploiting the relationship between the shape of the scatterer and angularly varying scattering amplitude, to extract discriminant features from these data. Under the assumption of uniform random length and uniform three-dimensional orientation distributions for each class of scatterers, the performance of several classification algorithms is evaluated. Simulations reveal that the incorporation of multiple angle data leads to a marked improvement in classification performance over single angle methods. The improvement is more substantial using broadband scatter. The simulations indicate that under the stated assumptions, a low classification error can be obtained. The use of multiple angle scatter therefore holds promise to substantially improve the in situ acoustic classification of fluid-like zooplankton using simple observation geometries.

© 2007 Acoustical Society of America

Acknowledgments

The authors would like to thank D. E. McGehee, M. Benfield, D. V. Holliday, and G. Greenlaw for development and maintenance of the Advanced Multifrequency Inversion Methods for Classifying Acoustic Scatters website, two anonymous reviews for helpful comments on the manuscript, and California Sea Grant for funding this research.

Article Outline

  1. INTRODUCTION
  2. FORWARD MODELING: THEORY AND NUMERICAL IMPLEMENTATION
    1. Multiple angle DWBA scattering model
    2. Scatterer size and orientation distributions
    3. Creation of model realizations
  3. FEATURE EXTRACTION
    1. Single frequency based feature space
    2. Discrete cosine transform based feature space
    3. Frequency correlation based feature space
  4. CLASSIFICATION OF FEATURES
  5. RESULTS AND DISCUSSION
  6. CONCLUSIONS AND FUTURE WORK

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KEYWORDS and PACS

Keywords

acoustic signal detection, underwater sound, bioacoustics, acoustic signal processing, microorganisms

PACS

  • 43.60.Np

    Acoustic signal processing techniques for neural nets and learning systems

  • 43.30.Sf

    Acoustical detection of marine life; passive and active

  • 43.60.Fg

    Acoustic array systems and processing, beam-forming

ARTICLE DATA

History
Received 08 Aug 2006
Accepted 24 Jan 2007
Revised 23 Jan 2007
Digital Object Identifier
http://dx.doi.org/10.1121/1.2697471

PUBLICATION DATA

ISSN

0001-4966 (print)  

Publisher

$abstract.society.value is a Member of CrossRef Acoustical Society of America

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