Bat Echolocation Call Detection and Species Recognition by Transformers with Self-attention

Bellafkir, Hicham; Vogelbacher, Markus; Gottwald, Jannis; Mühling, Markus; Korfhage, Nikolaus; Lampe, Patrick; Frieß, Nicolas; Nauß, Thomas; Freisleben, Bernd

doi:10.1007/978-3-031-08277-1_16

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…In the second phase, the detected calls are classified and assigned to the corresponding bat species. We have shown that our method outperforms state‐of‐the‐art CNN approaches for detecting bat calls and recognizing bat species in several publicly available datasets while achieving an average accuracy of up to 90.2% for detection and up to 88.7% mean average precision for recognition (Bellafkir et al., 2022).…”

Section: The Nature 40 Projectmentioning

confidence: 91%

“…while achieving an average accuracy of up to 90.2% for detection and up to 88.7% mean average precision for recognition (Bellafkir et al, 2022).…”

Section: Bat Species In Audio Recordingsmentioning

confidence: 99%

“…To address this need, we developed a new approach (Bellafkir et al., 2022, Figure 7) for detecting bat echolocation calls and recognizing bat species in audio spectrograms. Our method uses pretrained data‐efficient image transformer models that are used as components in a workflow we designed for processing audio spectrograms of recorded bat echolocation calls.…”

Section: The Nature 40 Projectmentioning

confidence: 99%

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Nature 4.0: A networked sensor system for integrated biodiversity monitoring

Zeuss,

Bald,

Gottwald

et al. 2023

Global Change Biology

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Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

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Section: The Nature 40 Projectmentioning

confidence: 91%

“…while achieving an average accuracy of up to 90.2% for detection and up to 88.7% mean average precision for recognition (Bellafkir et al, 2022).…”

Section: Bat Species In Audio Recordingsmentioning

confidence: 99%

Section: The Nature 40 Projectmentioning

confidence: 99%

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