Classifying Frog Calls Using Gaussian Mixture Models

Autor:	Eraldo Ribeiro, Kathryn Hollowood, Mark B. Bush, Katrina Smart, Dalwinderjeet Kular, Olatide Ommojaro
Rok vydání:	2015
Předmět:	Task (computing) Audio signal Computer science business.industry Feature extraction Process (computing) Spectrogram Pattern recognition Artificial intelligence Mixture model Focus (optics) business Image (mathematics)
Zdroj:	Advances in Visual Computing ISBN: 9783319278629 ISVC (2)
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::5a85b88fd09b9a45c6263b7b2910c238 https://doi.org/10.1007/978-3-319-27863-6_32 Zobrazit plný text záznamu