Using transfer learning with a convolutional neural network to detect African manatee (<i>Trichechus senegalensis</i>) vocalizations

Rycyk, Athena M.; Bolaji, Dunsin Abimbola; Factheu, Clinton; Takoukam, Aristide Kamla

doi:10.1121/10.0016543

JASA Express Letters

2022

DOI: 10.1121/10.0016543

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Using transfer learning with a convolutional neural network to detect African manatee (Trichechus senegalensis) vocalizations

Athena M. Rycyk

Dunsin Abimbola Bolaji

Clinton Factheu³

et al.

Abstract: African manatees ( Trichechus senegalensis) are vulnerable, understudied, and difficult to detect. Areas where African manatees are found were acoustically sampled and deep learning techniques were used to develop the first African manatee vocalization detector. A transfer learning approach was used to develop a convolutional neural network (CNN) using a pretrained CNN (GoogLeNet). The network was highly successful, even when applied to recordings collected from a different location. Vocal detections were more… Show more

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2024

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Cited by 2 publications

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Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Schall,

Kaya,

Debusschere

et al. 2024

Remote Sens Ecol Conserv

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Passive acoustic monitoring (PAM) is commonly used to obtain year‐round continuous data on marine soundscapes harboring valuable information on species distributions or ecosystem dynamics. This continuously increasing amount of data requires highly efficient automated analysis techniques in order to exploit the full potential of the available data. Here, we propose a benchmark, which consists of a public dataset, a well‐defined task and evaluation procedure to develop and test automated analysis techniques. This benchmark focuses on the special case of detecting animal vocalizations in a real‐world dataset from the marine realm. We believe that such a benchmark is necessary to monitor the progress in the development of new detection algorithms in the field of marine bioacoustics. We ultimately use the proposed benchmark to test three detection approaches, namely ANIMAL‐SPOT, Koogu and a simple custom sequential convolutional neural network (CNN), and report performances. We report the performance of the three detection approaches in a blocked cross‐validation fashion with 11 site‐year blocks for a multi‐species detection scenario in a large marine passive acoustic dataset. Performance was measured with three simple metrics (i.e., true classification rate, noise misclassification rate and call misclassification rate) and one combined fitness metric, which allocates more weight to the minimization of false positives created by noise. Overall, ANIMAL‐SPOT performed the best with an average fitness metric of 0.6, followed by the custom CNN with an average fitness metric of 0.57 and finally Koogu with an average fitness metric of 0.42. The presented benchmark is an important step to advance in the automatic processing of the continuously growing amount of PAM data that are collected throughout the world's oceans. To ultimately achieve usability of developed algorithms, the focus of future work should be laid on the reduction of the false positives created by noise.

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Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Schall,

Kaya,

Debusschere

et al. 2024

Remote Sens Ecol Conserv

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Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

Schneider,

von Fersen,

Dierkes

2024

Front. Conserv. Sci.

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The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats.

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Using transfer learning with a convolutional neural network to detect African manatee (Trichechus senegalensis) vocalizations

Cited by 2 publications

References 27 publications

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Deep learning in marine bioacoustics: a benchmark for baleen whale detection

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

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