International Symposium on Visual Computing

Advances in Visual Computing pp 347-354 | Cite as

Classifying Frog Calls Using Gaussian Mixture Models

  • Dalwinderjeet Kular
  • Kathryn Hollowood
  • Olatide Ommojaro
  • Katrina Smart
  • Mark Bush
  • Eraldo Ribeiro
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9475)

Abstract

We focus on the automatic classification of frog calls using shape features of spectrogram images. Monitoring frog populations is a means for tracking the health of natural habitats. This monitoring task is usually done by well-trained experts who listen and classify frog calls, which are tasks that are both time consuming and error prone. To automate this classification process, our method treats the sound signal of a frog call as a texture image, which is modeled as Gaussian mixture model. The method is simple but it has shown promising results. Tests performed on a dataset of frog calls of 15 different species produced an average classification rate of 80 %, which approximates human performance.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Dalwinderjeet Kular
    • 1
  • Kathryn Hollowood
    • 3
  • Olatide Ommojaro
    • 4
  • Katrina Smart
    • 1
  • Mark Bush
    • 2
  • Eraldo Ribeiro
    • 1
  1. 1.Computer Vision Laboratory, Department of Computer SciencesFlorida Institute of TechnologyMelbourneUSA
  2. 2.Department of Biological SciencesFlorida Institute of TechnologyMelbourneUSA
  3. 3.Department of Computer Science, Mathematics, and PhysicsRoberts Wesleyan CollegeRochesterUSA
  4. 4.Engineering, Mathematics, and Computer ScienceGeorgia Perimeter CollegeClarkstonUSA

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