website/_posts/research/2021-03-04-Recurrent_Primate_Classification.md

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single	Primate Vocalization Classification	research audio deep-learning anomalie-detection	A Deep and Recurrent Architecture	teaser assets/figures/11_recurrent_primate_workflow.jpg

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This study introduces a deep, recurrent architecture for classifying primate vocalizations, leveraging bidirectional Long Short-Term Memory networks and advanced techniques like normalized softmax and focal loss. Bayesian optimization was used to fine-tune hyperparameters, and the model was evaluated on a dataset of primate calls from an African sanctuary, showcasing the effectiveness of acoustic monitoring in wildlife conservation efforts. {% cite muller2021deep %}

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