Model-based detection of synthetic bat echolocation calls using an energy threshold detector for initialization

Abstract

Detection of echolocation calls is fundamental to quantitative analysis of bat acoustic signals. Automated methods of detection reduce the subjectivity of hand labeling of calls and speed up the detection process in an accurate and repeatable manner. A model-based detector was initialized using a baseline energy threshold detector, removing the need for hand labels to train the model, and shown to be superior to the baseline detector using synthetic calls in two experiments: (1) an artificial environment and (2) a field playback setting. Synthetic calls using a piecewise exponential frequency modulation function from five hypothetical species were employed to control the signal-to-noise ratio (SNR) in each experiment and to provide an absolute ground truth to judge detector performance. The model-based detector outperformed the baseline detector by 2.5 dB SNR in the artificial environment and 1.5 dB SNR in the field playback setting. Atmospheric absorption was measured for the synthetic calls, and 1.5 dB increased the effective detection radius by between 1 and 7 m depending on species. The results demonstrate that hand labels are not necessary for training detection models and that model-based detectors significantly increase the range of detection for a recording system.