Mel-frequency cepstral coefficients outperform embeddings from pre-trained convolutional neural networks under noisy conditions for discrimination tasks of individual gibbons

Lakdari, Mohamed Walid; Ahmad, Abdul Hamid; Sethi, Sarab; Bohn, Gabriel A.; Clink, Dena J.

doi:10.1016/j.ecoinf.2023.102457

Cited by 4 publications

References 77 publications

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Applying machine learning to primate bioacoustics: Review and perspectives

Cauzinille,

Favre,

Marxer

et al. 2024

American J Primatol

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This paper provides a comprehensive review of the use of computational bioacoustics as well as signal and speech processing techniques in the analysis of primate vocal communication. We explore the potential implications of machine learning and deep learning methods, from the use of simple supervised algorithms to more recent self‐supervised models, for processing and analyzing large data sets obtained within the emergence of passive acoustic monitoring approaches. In addition, we discuss the importance of automated primate vocalization analysis in tackling essential questions on animal communication and highlighting the role of comparative linguistics in bioacoustic research. We also examine the challenges associated with data collection and annotation and provide insights into potential solutions. Overall, this review paper runs through a set of common or innovative perspectives and applications of machine learning for primate vocal communication analysis and outlines opportunities for future research in this rapidly developing field.

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Applying machine learning to primate bioacoustics: Review and perspectives

Cauzinille,

Favre,

Marxer

et al. 2024

American J Primatol

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Meta-Embedded Clustering (MEC): A new method for improving clustering quality in unlabeled bird sound datasets

Poutaraud,

Sueur,

Thébaud

et al. 2024

Ecological Informatics

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Benchmarking automated detection and classification approaches for monitoring of endangered species: a case study on gibbons from Cambodia

Clink,

Cross-Jaya,

Kim

et al. 2024

Preprint

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Recent advances in deep and transfer learning have revolutionized our ability for the automated detection and classification of acoustic signals from long-term recordings. Here, we provide a benchmark for the automated detection of southern yellow-cheeked crested gibbon (Nomascus gabriellae) calls collected using autonomous recording units (ARUs) in Andoung Kraleung Village, Cambodia. We compared the performance of support vector machines (SVMs), a quasi-DenseNet architecture (Koogu), transfer learning with pretrained convolutional neural network (ResNet50) models trained on the ImageNet dataset, and transfer learning with embeddings from a global birdsong model (BirdNET) based on an EfficientNet architecture. We also investigated the impact of varying the number of training samples on the performance of these models. We found that BirdNET had superior performance with a smaller number of training samples, whereas Koogu and ResNet50 models only had acceptable performance with a larger number of training samples (>200 gibbon samples). Effective automated detection approaches are critical for monitoring endangered species, like gibbons. Future work on other gibbon species will be informative. Code and data are publicly available for future benchmarking.

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Mel-frequency cepstral coefficients outperform embeddings from pre-trained convolutional neural networks under noisy conditions for discrimination tasks of individual gibbons

Cited by 4 publications

References 77 publications

Applying machine learning to primate bioacoustics: Review and perspectives

Applying machine learning to primate bioacoustics: Review and perspectives

Meta-Embedded Clustering (MEC): A new method for improving clustering quality in unlabeled bird sound datasets

Benchmarking automated detection and classification approaches for monitoring of endangered species: a case study on gibbons from Cambodia

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