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2015 | OriginalPaper | Buchkapitel

Classifying Frog Calls Using Gaussian Mixture Models

verfasst von : Dalwinderjeet Kular, Kathryn Hollowood, Olatide Ommojaro, Katrina Smart, Mark Bush, Eraldo Ribeiro

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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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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Titel: Classifying Frog Calls Using Gaussian Mixture Models
verfasst von: Dalwinderjeet Kular
Kathryn Hollowood
Olatide Ommojaro
Katrina Smart
Mark Bush
Eraldo Ribeiro
Verlag: Springer International Publishing
Buch: Advances in Visual Computing
Print ISBN: 978-3-319-27862-9

Electronic ISBN: 978-3-319-27863-6

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-27863-6_32

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