Species-specific audio detection: A comparison of three template-based classification algorithms using random forests

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1 Department of Computer Science, University of Puerto Rico - Rio Piedras, San Juan, Puerto Rico
2 Sieve Analytics, Inc., San Juan, Puerto Rico
3 Department of Biology, University of Puerto Rico - Río Piedras, San Juan, Puerto Rico
DOI
10.7287/peerj.preprints.2713v1
Published
Accepted
Subject Areas
Bioinformatics, Computational Biology
Keywords
Acoustic monitoring, Machine learning, Animal vocalizations, Recording visualization, Recording annotation, Generic species algorithm, web-based cloud-hosted system, Random Forest classifier, species prediction, Species-specific audio detection
Copyright
© 2017 Corrada Bravo et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Corrada Bravo CJ, Álvarez Berríos R, Aide TM. 2017. Species-specific audio detection: A comparison of three template-based classification algorithms using random forests. PeerJ Preprints 5:e2713v1

Abstract

We developed a web-based cloud-hosted system that allow users to archive, listen, visualize, and annotate recordings. The system also provides tools to convert these annotations into datasets that can be used to train a computer to detect the presence or absence of a species. The algorithm used by the system was selected after comparing the accuracy and efficiency of three variants of a template-based classification. The algorithm computes a similarity vector by comparing a template of a species call with time increments across the spectrogram. Statistical features are extracted from this vector and used as input for a Random Forest classifier that predicts presence or absence of the species in the recording. The fastest algorithm variant had the highest average accuracy and specificity; therefore, it was implemented in the ARBIMON web-based system.

Author Comment

This is a submission to PeerJ Computer Science for review.