Deep Multiple Instance Learning Ensemble for the Acoustic Detection of Tropical Birds

Castro, Jorge; Vargas-Masis, Roberto; Rojas, Danny Alfaro

doi:10.1109/icmla51294.2020.00051

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Semi-automatic classification methods, such as Cubic SVM and RF, also have high accuracy rates of about 90% in identifying bird calls by combining human expertise with machine learning algorithms to handle complex patterns in acoustic data [27]. The deep Multiple Instance Learning (MIL) frameworks, however, have a lower accuracy of 0.77 (F1-score) compared to GMM and syllable-based models [28]. The hidden Markov model/Gaussian mixture model (HMM/GMM) classifier has the highest performance metrics for correct accuracy (VL02) due to its ability to handle complex vocalization patterns better [29] (Table 1).…”

Section: Summary On Bioacoustics Monitoring Of Forest Environments Am...mentioning

confidence: 99%

A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques

2023

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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 for computer vision and interpretation. Despite the increasing use of AI in wildlife ecology, its future in acoustic wildlife monitoring remains uncertain. To assess its potential and identify future needs, a scientific literature review was conducted on 54 works published between 2015 and March 2022. The results of the review showed a significant rise in the utilization of AI techniques in wildlife acoustic monitoring over this period, with birds (N = 26) gaining the most popularity, followed by mammals (N = 12). The most commonly used AI algorithm in this field was Convolutional Neural Network, which was found to be more accurate and beneficial than previous categorization methods in acoustic wildlife monitoring. This highlights the potential for AI to play a crucial role in advancing our understanding of wildlife populations and ecosystems. However, the results also show that there are still gaps in our understanding of the use of AI in wildlife acoustic monitoring. Further examination of previously used AI algorithms in bioacoustics research can help researchers better understand patterns and identify areas for improvement in autonomous wildlife monitoring. In conclusion, the use of AI in wildlife acoustic monitoring is a rapidly growing field with a lot of potential. While significant progress has been made in recent years, there is still much to be done to fully realize the potential of AI in this field. Further research is needed to better understand the limitations and opportunities of AI in wildlife acoustic monitoring, and to develop new algorithms that can improve the accuracy and usefulness of this technology.

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Section: Summary On Bioacoustics Monitoring Of Forest Environments Am...mentioning

confidence: 99%

A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques

2023

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Acoustic detection of Red-capped Manakin (Ceratopipra mentalis) in Sarapiquí, Costa Rica

Vargas-Masis¹,

Sequeira²,

Mendoza-Garro

et al. 2021

2021 IEEE 3rd International Conference on BioInspired Processing (BIP)

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Deep Multiple Instance Learning Ensemble for the Acoustic Detection of Tropical Birds

Cited by 2 publications

References 21 publications

A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques

A Methodological Literature Review of Acoustic Wildlife Monitoring Using Artificial Intelligence Tools and Techniques

Acoustic detection of Red-capped Manakin (Ceratopipra mentalis) in Sarapiquí, Costa Rica

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