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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Acknowledgments
The authors acknowledge support from National Science Foundation (NSF) grants No. 1263011 and No. 1152306. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.
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Kular, D., Hollowood, K., Ommojaro, O., Smart, K., Bush, M., Ribeiro, E. (2015). Classifying Frog Calls Using Gaussian Mixture Models. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_32
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DOI: https://doi.org/10.1007/978-3-319-27863-6_32
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