Primate Vocalization Classification

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Reference

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

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Acoustic monitoring offers a powerful, non-invasive tool for wildlife conservation, enabling the study and tracking of animal populations through their vocalizations. This research focuses on improving the automated classification of primate vocalizations, a challenging task due to call variability and environmental noise.

We propose a novel deep, recurrent neural network architecture specifically designed for this purpose. The core of the model utilizes bidirectional Long Short-Term Memory (BiLSTM) networks, which are adept at capturing temporal dependencies within the audio signals (represented, for example, as spectrograms or MFCCs).

To further enhance classification performance, particularly in potentially imbalanced datasets common in bioacoustics, the architecture incorporates advanced techniques:

• Normalized Softmax: Improves calibration and potentially robustness.
• Focal Loss: Addresses class imbalance by focusing training on hard-to-classify examples.

Hyperparameter tuning, a critical step for optimizing deep learning models, was systematically performed using Bayesian optimization.

Performance results demonstrating classification accuracy.

The model’s effectiveness was evaluated on a challenging real-world dataset comprising diverse primate calls recorded at an African wildlife sanctuary. The results demonstrate the capability of the proposed deep recurrent architecture for accurate primate vocalization classification, underscoring the potential of advanced deep learning techniques combined with automated acoustic monitoring for practical wildlife conservation efforts. [Müller et al. 2021]