Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

Lehnhoff, Loïc; Glotin, Hervé; Bernard, Serge; Dabin, Willy; Gall, Yves Le Le; Menut, E.; Méheust, Eleonore; Peltier, Hélène; Pochat, Alain; Pochat, Krystel; Rimaud, Thomas; Sourget, Quiterie; Spitz, Jérôme; Canneyt, Olivier Van; Mérigot, Bastien

doi:10.3390/su142013186

Cited by 3 publications

(3 citation statements)

References 91 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Marine sustainability and the well-being of dolphins are interconnected because dolphins are marine mammals that depend on healthy and sustainable marine ecosystems for their survival. Ensuring the sustainability of marine environments is crucial for protecting dolphins and other marine species [2]. Dolphins have always been a subject of fascination for biologists and ecologists because of their highly intelligent nature and complex social behavior [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Tseng,

Hsu,

Wang

et al. 2024

JMSE

View full text Add to dashboard Cite

Caring for dolphins is a delicate process that requires experienced caretakers to pay close attention to their behavioral characteristics. However, caretakers may sometimes lack experience or not be able to give their full attention, which can lead to misjudgment or oversight. To address these issues, a dolphin behavior analysis system has been designed to assist caretakers in making accurate assessments. This study utilized image preprocessing techniques to reduce sunlight reflection in the pool and enhance the outline of dolphins, making it easier to analyze their movements. The dolphins were divided into 11 key points using an open-source tool called DeepLabCut, which accurately helped mark various body parts for skeletal detection. The AquaAI Dolphin Decoder (ADD) was then used to analyze six dolphin behaviors. To improve behavior recognition accuracy, the long short-term memory (LSTM) neural network was introduced. The ADD and LSTM models were integrated to form the ADD-LSTM system. Several classification models, including unidirectional and bidirectional LSTM, GRU, and SVM, were compared. The results showed that the ADD module combined with a double-layer bidirectional LSTM method achieved high accuracy in dolphin behavior analysis. The accuracy rates for each behavior exceeded 90%.

show abstract

Section: Introductionmentioning

confidence: 99%

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Tseng,

Hsu,

Wang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…sounds for communication, reproduction, foraging, and navigational purposes. The acoustic communication of dolphins employs a wide range of vocalizations, including clicks, burst-pulses, buzzes, and whistles [11]. Whistles, in particular, serve various social functions such as individual identification, group cohesion, and coordination of activities, such as feeding, resting, socializing, and navigation [12].…”

Section: Introductionmentioning

confidence: 99%

Building Ensemble of Resnet for Dolphin Whistle Detection

Nanni¹,

Cuza²,

Brahnam³

2023

Preprint

View full text Add to dashboard Cite

To effectively preserve marine environments and manage endangered species, it is necessary to employ efficient, precise, and scalable solutions for environmental monitoring. Ecoacoustics provides several benefits as it enables non-intrusive, prolonged sampling of environmental sounds, making it a promising tool for conducting biodiversity surveys. However, analyzing and interpreting acoustic data can be time-consuming and often demands substantial human supervision. This challenge can be addressed by harnessing contemporary methods for automated audio signal analysis, which have exhibited remarkable performance due to advancements in deep learning research. This paper introduces a research investigation into developing an automatic computerized system to detect dolphin whistles. The proposed method utilizes a fusion of various resnet50 networks integrated with data augmentation techniques. Through extensive experiments conducted on a publically available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across all evaluated metrics. The MATLAB/PyTorch source code is freely available at: https://github.com/LorisNanni/

show abstract

“…Like many other marine mammals, dolphins are acoustic specialists that rely on sounds for communication, reproduction, foraging, and navigational purposes. The acoustic communication of dolphins employs a wide range of vocalizations, including clicks, burst-pulses, buzzes, and whistles [11]. Whistles, in particular, serve various social functions such as individual identification, group cohesion, and coordination of activities, such as feeding, resting, Appl.…”

Section: Introductionmentioning

confidence: 99%

Building Ensemble of Resnet for Dolphin Whistle Detection

2023

View full text Add to dashboard Cite

Ecoacoustics is arguably the best method for monitoring marine environments, but analyzing and interpreting acoustic data has traditionally demanded substantial human supervision and resources. These bottlenecks can be addressed by harnessing contemporary methods for automated audio signal analysis. This paper focuses on the problem of assessing dolphin whistles using state-of-the-art deep learning methods. Our system utilizes a fusion of various resnet50 networks integrated with data augmentation (DA) techniques applied not to the training data but to the test set. We also present training speeds and classification results using DA to the training set. Through extensive experiments conducted on a publicly available benchmark, our findings demonstrate that our ensemble yields significant performance enhancements across several commonly used metrics. For example, our approach obtained an accuracy of 0.949 compared to 0.923, the best reported in the literature. We also provide training and testing sets that other researchers can use for comparison purposes, as well as all the MATLAB/PyTorch source code used in this study.

show abstract

Behavioural Responses of Common Dolphins Delphinus delphis to a Bio-Inspired Acoustic Device for Limiting Fishery By-Catch

Cited by 3 publications

References 91 publications

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

An Integrated Framework with ADD-LSTM and DeepLabCut for Dolphin Behavior Classification

Building Ensemble of Resnet for Dolphin Whistle Detection

Building Ensemble of Resnet for Dolphin Whistle Detection

Contact Info

Product

Resources

About