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

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

Blog Article

We developed a web-based cloud-hosted system that allow users to archive, listen, LUCID DREAM 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 ACCESSORIES 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.