Towards a General Approach for Bat Echolocation Detection and Classification

Abstract

Acoustic monitoring is an effective and scalable way to assess the health of important bioindicators like bats in the wild. However, the large amounts of resulting noisy data requires accurate tools for automatically determining the presence of different species of interest. Machine learning-based solutions offer the potential to reliably perform this task, but can require expertise in order to train and deploy. We propose BatDetect2, a novel deep learning-based pipeline for jointly detecting and classifying bat species from acoustic data. Distinct from existing deep learning-based acoustic methods, BatDetect2's outputs are interpretable as they directly indicate at what time and frequency a predicted echolocation call occurs. BatDetect2 also makes use of surrounding temporal information in order to improve its predictions, while still remaining computationally efficient at deployment time. We present experiments on five challenging datasets, from four distinct geographical regions (UK, Mexico, Australia, and Brazil). BatDetect2 results in a mean average precision of 0.88 for a dataset containing 17 bat species from the UK. This is significantly better than the 0.71 obtained by a traditional call parameter extraction baseline method. We show that the same pipeline, without any modifications, can be applied to acoustic data from different regions with different species compositions. The data annotation, model training, and evaluation tools proposed will enable practitioners to easily develop and deploy their own models. BatDetect2 lowers the barrier to entry preventing researchers from availing of effective deep learning bat acoustic classifiers.

Overcoming biodiversity blindness: Secondary data in primary citizen science observations

“…and BatDetect2 and BatNet for bats (Aodha et al, 2022;Krivek et al, 2023) in sound recordings. Additionally, customised models can be trained on open datasets, for example, FathomNet for marine organisms (Katija et al, 2022), Pl@ntNet for plants and iNaturalist for a range of different species.…”

“…Moreover, models exist for specific groups of organisms and data types: Merlin Bird ID and BirdNET (Kahl et al., 2021) for bird detection based on sound (the former can identify species also from images), Pl@ntNet API for plants, Bjerge et al. (2023) created a test dataset for insects, FishID for fish species in images; MegaDetector or TrapTagger for animals in camera trap photos; and BatDetect2 and BatNet for bats (Aodha et al., 2022; Krivek et al., 2023) in sound recordings. Additionally, customised models can be trained on open datasets, for example, FathomNet for marine organisms (Katija et al., 2022), Pl@ntNet for plants and iNaturalist for a range of different species.…”

An Efficient Neural Network Design Incorporating Autoencoders for the Classification of Bat Echolocation Sounds

“…The great advantage of (deep) neural networks is their strong ability to find meaningful patterns inside the training data, making the need for feature engineering mostly irrelevant. The first deep-learning-based model to detect bat echolocation sounds from audio data was Bat Detective [ 31 ], which was recently expanded into a joint model that performs bat sound detection and species identification [ 32 ]. The first iteration consists of two versions of a CNN trained on single bat pulses.…”

“…For the prediction step, it moves in a sliding-window fashion along the spectrogram in order to detect individual pulses with a window size of 23 ms [ 31 ]. The second iteration is a CNN performing complete end-to-end bounding-box detection for spectrograms of less than two seconds [ 32 ]. This approach allows a model to learn temporal dependencies between consecutive bat pulses.…”