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

• Main Authors: Carlos J. Corrada Bravo, Rafael Álvarez Berríos, T. Mitchell Aide
• Format: Article
• Language: English
• Published: PeerJ Inc. 2017-04-01
• Series: PeerJ Computer Science
• Subjects: Acoustic monitoring; Machine learning; Animal vocalizations; Recording visualization; Recording annotation; Generic species algorithm
• Online Access: https://peerj.com/articles/cs-113.pdf

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 detection. 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.