Applications with challenges and future directions for bioacoustics monitoring amidst AI.

Applications with challenges and future directions for bioacoustics monitoring amidst AI.

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Artificial intelligence (AI) has become a significantly growing field in the environmental sector due to its ability to solve problems, make decisions, and recognize patterns. The significance of AI in wildlife acoustic monitoring is particularly important because of the vast amounts of data that are available in this field, which can be leveraged...

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... technology has been applied in various aspects of acoustic wildlife monitoring including behavioral patterns [22,23,26], species recognition [24,25,29], species density/population estimation, species diversity [30], and illegal trade [32] (Table 1 and Figure 3). These applications have helped researchers understand the location and migration patterns of animals, as well as their social group affiliations. ...

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... Passive acoustic monitoring represents an innovative tool for recording both soundproducing species and environments (Do Nascimento et al., 2020; S. Sharma et al., 2023;Sugai et al., 2019;Usman et al., 2020). This tool allows researchers to survey species over large spatial-temporal scales while minimising the fieldwork efforts (Hoyos-Cardona et al., 2021). ...
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... Traditional methods, such as manual surveys and visual inspections, rely heavily on human expertise and can be prone to errors, particularly in challenging environments where species are difficult to detect (Beijbom et al., 2015). In contrast, AI-based methods can process and analyze vast amounts of data quickly and accurately, enabling the detection of invasive species in real-time and across large areas (Sharma et al., 2023). ...
... Additionally, collection of information using ARUs can result in large datasets, which can be incredibly time consuming and labor intensive to clean, code, and analyze to extract meaningful trends (Priyadarshani et al. 2018). New technologies, such as the development of Artificial Intelligence (AI), are able to overcome some of these limitations by assisting in data cleaning and analysis (Sharma et al. 2023), but trained human observers are still more proficient at identifying misclassifications in the datasets. ...
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