Animal Sound Classification using Sequential Classifiers

Javier Romero, Amalia Luque, Alejandro Carrasco


Several authors have shown that the sounds of anurans can be used as an indicator of climate change. But the recording, storage and further processing of a huge number of anuran's sounds, distributed in time and space, are required to obtain this indicator. It is therefore highly desirable to have algorithms and tools for the automatic classification of the different classes of sounds. In this paper five different classification methods are proposed, all of them based on the data mining domain, which try to take advantage of the sound sequential behaviour. Its definition and comparison is undertaken using several approaches. The sequential classifiers have revealed that they can obtain a better performance than their non-sequential counterpart. The sliding window with an underlying decision tree has reached the best results in our tests, even overwhelming the Hidden Markov Models usually employed in similar applications. A quite remarkable overall classification performance has been obtained, a result even more relevant considering the low quality of the analysed sounds.


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Paper Citation

in Harvard Style

Romero J., Luque A. and Carrasco A. (2017). Animal Sound Classification using Sequential Classifiers . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017) ISBN 978-989-758-212-7, pages 242-247. DOI: 10.5220/0006246002420247

in Bibtex Style

author={Javier Romero and Amalia Luque and Alejandro Carrasco},
title={Animal Sound Classification using Sequential Classifiers},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)},

in EndNote Style

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, (BIOSTEC 2017)
TI - Animal Sound Classification using Sequential Classifiers
SN - 978-989-758-212-7
AU - Romero J.
AU - Luque A.
AU - Carrasco A.
PY - 2017
SP - 242
EP - 247
DO - 10.5220/0006246002420247