DOI: 10.5061/dryad.4xgxd25fh
title: Open‐source workflow approaches to passive acoustic monitoring of bats

description: The affordability, storage, and power capacity of compact modern recording
 hardware has evolved passive acoustic monitoring (PAM) of animals and
 soundscapes into a non-invasive, cost-effective tool for research and
 ecological management and is particularly effective for bats and toothed
 whales that consistently echolocate. The use of  PAM at large
 scales hinges on effective automated detectors and species classifiers
 which, combined with distance sampling approaches, have enabled species
 abundance estimation of toothed whales. But standardized, user-friendly,
 and open-access automated detection and classification workflows are in
 demand for this key conservation metric to be realized for bats. We used
 the PAMGuard toolbox including its new deep learning
 classification module to test the performance of four open-source
 workflows for automated analyses of acoustic datasets from bats. Each
 workflow used a different initial detection algorithm followed by the same
 deep learning classification algorithm and was evaluated against the
 performance of an expert manual analyst. Workflow performance depended
 strongly on the signal-to-noise ratio and detection algorithm used: the
 full deep learning workflow had the best classification accuracy (≤67%)
 but was computationally too slow for practical large-scale bat PAM.
 Workflows using PAMGuard’s detection module or triggers onboard an SM4BAT
 or AudioMoth accurately classified up to 47%, 59% and 34%, respectively,
 of calls to species. Not all workflows included noise sampling critical to
 estimating changes in detection probability over time, a vital parameter
 for abundance estimation. The workflow using PAMGuard’s detection module
 was 40 times faster than the full deep learning workflow and missed as few
 calls (recall for both ~0.6), thus balancing computational speed and
 performance.  We show that complete acoustic detection and
 classification workflows for bat PAM data can be efficiently automated
 using open-source software such as PAMGuard and exemplify how detection
 choices, whether pre- or post-deployment, hardware or software-driven,
 affect the performance of deep learning classification and the downstream
 ecological information that can be extracted from acoustic recordings. In
 particular, understanding, and quantifying detection/classification
 accuracy and the probability of detection are key to avoid introducing
 biases that may ultimately affect the quality of data for ecological
 management.
description: <strong>Brief
 Overview</strong>: These data include raw and processed acoustic
 recordings of bats at woodland edges at different locations in Denmark,
 made with two common types of acoustic devices: the proprietary SM4BAT FS
 running firmware version 2.2.7 [Wildlife Acoustics Inc., Maynard, MA,
 USA], and the open-source AudioMoth running firmware version 1.4.4 (Hill
 et al., 2018, openacousticdevices.org).
 <strong>Study Context</strong>: The study focus was to test how different setups
 for detection of bat calls affect the performance of complete acoustic
 detection and classification workflows for bat PAM data and to demonstrate
 such full, open-source workflows using PAMGuard (www.pamguard.org)<br><br>Each of the four workflows depended on a different initial detection algorithm: 1) A deep learning detection model based on ANIMAL-SPOT and integrated in PAMGuard, 2) PAMGuard's click detector module, 3) a device-based static trigger on the open source AudioMoth recorder, and 4) a device-based dynamic trigger on the proprietary SM4BAT FS recorder. Data from each workflow was processed in PAMGuard. For the two workflows using the AudioMoth and SM4BAT recorders, detections were imported into PAMGUARD for further processing. Following the detection stage, each workflow used the same deep learning species classifier integrated in PAMGuard from ANIMAL-SPOT. 
description: The project made extensive use of Raven, PAMGuard to process
 files and AnimalSpot to develop deep learning models. 
 Raven Lite was used to annotate files; it is not open source but
 can be downloaded free of charge from www.ravensoundsoftware.com. Sonic Visualiser provides an open-source alternative to Raven and can be downloaded from www.sonicvisualiser.org. PAMGuard was used for automated processing and can be downloaded from www.pamguard.org.  AnimalSpot is a Python library used to train deep learning models and can be forked from here with a paper describing capabilities here. The files supplied in the dataset are generally text files and raw sound files - they can be opened with a large number of programs.