Automated acoustic detection

Traditional human-observer-based biological surveys are expensive. Therefore most biodiversity studies are implemented only periodically, for short periods, and predominantly during daytime and under favorable weather conditions. Automated data acquisition and analysis can overcome these shortcomings and facilitate continuous monitoring. Here we report on the development of an automated acoustic recognizer for Southern Lapwing Vanellus chilensis lampronotus vocalizations, a first for this species. The recognizer is a species-specific information retrieval agent, which searches throughout long audio recordings in order to detect and timestamp call events of the target species. The recognizer relies on a log-likelihood ratio estimator, based on a Gaussian Mixture Model–Universal Background Model (GMM–UBM), complemented with purposely-developed temporal post-processing that incorporates domain knowledge about the structure of V. chilensis vocalizations. Validation experiments with real-field recordings of complex soundscapes indicate that the recognizer is sensitive enough to register V. chilensis call events with sound levels down to 30 dB and recognition accuracy of up to 85.6%, at zero false positive
rates. The recognizer is considered a valuable tool for computer-assisted analysis of hourly and daily acoustic activity of V. chilensis over extended periods of time, as it offers an indispensable support to longterm monitoring studies and conservation efforts in the Pantanal region.