Detecting Bat Calls from Audio Recordings

Bat monitoring is commonly based on audio analysis. By collecting audio recordings from large areas and analysing their content, it is possible estimate distributions of bat species and changes in them. It is easy to collect a large amount of audio recordings by leaving automatic recording units in nature and collecting them later. However, it takes a lot of time and effort to analyse these recordings. Because of that, there is a great need for automatic tools. We developed a program for detecting bat calls automatically from audio recordings. The program is designed for recordings that are collected from Finland with the AudioMoth recording device. Our method is based on a median clipping method that has previously shown promising results in the field of bird song detection. We add several modifications to the basic method in order to make it work well for our purpose. We use real-world field recordings that we have annotated to evaluate the performance of the detector and compare it to two other freely available programs (Kaleidoscope and Bat Detective). Our method showed good results and got the best F2-score in the comparison

Shazam For Bats: Internet of Things for Continuous Real-Time Biodiversity Monitoring

Biodiversity surveys are often required for development projects in cities that could affect protected species such as bats. Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report on the performance of mitigation actions. Typically, sensors are used in the field to listen to the ultrasonic echolocation calls of bats or the audio data is recorded for post-processing to calculate the activity levels. Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field. Here, we show the results from a longitudinal study of 15 novel Internet connected bat sensors—Echo Boxes—in a large urban park. The study provided empirical evidence of how edge processing can reduce network traffic and storage demands by several orders of magnitude, making it possible to run continuous monitoring activities for many months including periods which traditionally would not be monitored. Our results demonstrate how the combination of artificial intelligence techniques and low-cost sensor networks can be used to create novel insights for ecologists and conservation decision-maker

Automatic Bat Call Classification using Transformer Networks

Automatically identifying bat species from their echolocation calls is a difficult but important task for monitoring bats and the ecosystem they live in. Major challenges in automatic bat call identification are high call variability, similarities between species, interfering calls and lack of annotated data. Many currently available models suffer from relatively poor performance on real-life data due to being trained on single call datasets and, moreover, are often too slow for real-time classification. Here, we propose a Transformer architecture for multi-label classification with potential applications in real-time classification scenarios. We train our model on synthetically generated multi-species recordings by merging multiple bats calls into a single recording with multiple simultaneous calls. Our approach achieves a single species accuracy of 88.92% (F1-score of 84.23%) and a multi species macro F1-score of 74.40% on our test set. In comparison to three other tools on the independent and publicly available dataset ChiroVox, our model achieves at least 25.82% better accuracy for single species classification and at least 6.9% better macro F1-score for multi species classification.Comment: Volume 78, December 2023, 10228

Open-source workflow approaches to passive acoustic monitoring of bats

The work was funded by grants to PTM from Carlsberg Semper Ardens Research Projects and the Independent Research Fund Denmark.The affordability, storage and power capacity of compact modern recording hardware have evolved passive acoustic monitoring (PAM) of animals and soundscapes into a non-invasive, cost-effective tool for research and ecological management particularly useful for bats and toothed whales that orient and forage using ultrasonic echolocation. 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.Publisher PDFPeer reviewe

Efficient Bird Sound Detection on the Bela Embedded System

© 2020 IEEE. Monitoring wildlife is an important aspect of conservation initiatives. Deep learning detectors can help with this, although it is not yet clear whether they can run efficiently on an embedded system in the wild. This paper proposes an automatic detection algorithm for the Bela embedded Linux device for wildlife monitoring. The algorithm achieves good quality recognition, efficiently running on continuously streamed data on a commercially available platform. The program is capable of computing on-board detection using convolutional neural networks (CNNs) with an AUC score of 82.5% on the testing set of an international data challenge. This paper details how the model is exported to work on the Bela Mini in C++, with the spectrogram generation and the implementation of the feed-forward network, and evaluates its performance on the Bird Audio Detection challenge 2018 DCASE data

Whombat: An open-source annotation tool for machine learning development in bioacoustics

1. Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high-stakes applications, such as conservation research, demands a data-centric approach with a focus on utilizing carefully annotated and curated evaluation and training data that is relevant and representative. Creating annotated datasets of sound recordings presents a number of challenges, such as managing large collections of recordings with associated metadata, developing flexible annotation tools that can accommodate the diverse range of vocalization profiles of different organisms, and addressing the scarcity of expert annotators. 2. We present Whombat a user-friendly, browser-based interface for managing audio recordings and annotation projects, with several visualization, exploration, and annotation tools. It enables users to quickly annotate, review, and share annotations, as well as visualize and evaluate a set of machine learning predictions on a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality. 3. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: an project aimed at enhancing automated UK bat call identification at the Bat Conservation Trust (BCT), and a collaborative effort among the USDA Forest Service and Oregon State University researchers exploring bioacoustic applications and extending automated avian classification models in the Pacific Northwest, USA. 4. Whombat is a flexible tool that can effectively address the challenges of annotation for bioacoustic research. It can be used for individual and collaborative work, hosted on a shared server or accessed remotely, or run on a personal computer without the need for coding skills.Comment: 17 pages, 2 figures, 2 tables, to be submitted to Methods in Ecology and Evolutio

Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring

1. High-throughput environmental sensing technologies are increasingly central to global monitoring of the ecological impacts of human activities. In particular, the recent boom in passive acoustic sensors has provided efficient, noninvasive, and taxonomically broad means to study wildlife populations and communities, and monitor their responses to environmental change. However, until recently, tech-nological costs and constraints have largely confined research in passive acoustic monitoring (PAM) to a handful of taxonomic groups (e.g., bats, cetaceans, birds), often in relatively small-scale, proof-of-concept studies.2. The arrival of low-cost, open-source sensors is now rapidly expanding access to PAM technologies, making it vital to evaluate where these tools can contribute to broader efforts in ecology and biodiversity research. Here, we synthesise and critically assess the current emerging opportunities and challenges for PAM for ecological assessment and monitoring of both species populations and communities.3. We show that terrestrial and marine PAM applications are advancing rapidly, fa-cilitated by emerging sensor hardware, the application of machine learning inno-vations to automated wildlife call identification, and work towards developing acoustic biodiversity indicators. However, the broader scope of PAM research remains constrained by limited availability of reference sound libraries and open-source audio processing tools, especially for the tropics, and lack of clarity around the accuracy, transferability and limitations of many analytical methods.4. In order to improve possibilities for PAM globally, we emphasise the need for col-laborative work to develop standardised survey and analysis protocols, publicly archived sound libraries, multiyear audio datasets, and a more robust theoretical and analytical framework for monitoring vocalising animal communities

Impacts of coffee fragmented landscapes on biodiversity and microclimate with emerging monitoring technologies

Habitat fragmentation and loss are causing biodiversity declines across the globe. As biodiversity is unevenly distributed, with many hotspots located in the tropics, conserving and protecting these areas is important to preserve as many species as possible. Chapter 2 presents an overview of the Ecology of the Atlantic Forest, a highly fragmented biodiversity hotspot. A major driver of habitat fragmentation is agriculture, and in the tropics coffee is major cash crop. Developing methods to monitor biodiversity effectively without labour intensive surveys can help us understand how communities are using fragmented landscapes and better inform management practices that promote biodiversity. Acoustic monitoring offers a promising set of tools to remotely monitor biodiversity. Developments in machine learning offer automatic species detection and classification in certain taxa. Chapters 3 and 4 use acoustic monitoring surveys conducted on fragmented landscapes in the Atlantic Forest to quantify bird and bat communities in forest and coffee matrix, respectively. Chapter 3 shows that acoustic composition can reflect local avian communities. Chapter 4 applies a convolutional neural network (CNN) optimised on UK bat calls to a Brazilian bat dataset to estimate bat diversity and show how bats preferentially use coffee habitats. In addition to monitoring biodiversity, monitoring microclimate forms a key part of climate smart agriculture for climate change mitigation. Coffee agriculture is limited to the tropics, overlapping with biodiverse regions, but is threatened by climate change. This presents a challenge to countries strongly reliant on coffee exports such as Brazil and Nicaragua. Chapter 5 uses data from microclimate weather stations in Nicaragua to demonstrate that sun-coffee management is vulnerable to supraoptimal temperature exposure regardless of local forest cover or elevation.Open Acces

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technologi- cal advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaborationpublishedVersio