---
layout: single
title:  "Primate Vocalization Classification"
categories: research audio deep-learning anomalie-detection 
excerpt: "A Deep and Recurrent Architecture"
header:
  teaser: assets/figures/11_recurrent_primate_workflow.jpg
---

![Leak-Mels](\assets\figures\11_recurrent_primate_workflow.jpg){:style="display:block; width:40%" .align-right}

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 %}

![Approach](\assets\figures\11_recurrent_primate_results.jpg){:style="display:block; width:85%" .align-center}