1. Müller, R., Illium, S., and Linnhoff-Popien, C. 2021. A Deep and Recurrent Architecture for Primate Vocalization Classification. Proc. Interspeech 2021, 461–465.


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. [Müller et al. 2021]