# Convolutional Recurrent Neural Networks for Bird Audio Detection

## TL;DR

This paper introduces a convolutional recurrent neural network approach for automated bird audio detection, effectively capturing spectral features and temporal dependencies, achieving high accuracy in real-world environments.

## Contribution

It presents a novel combination of convolutional and recurrent layers specifically designed for bird sound detection in challenging real-life conditions.

## Key findings

- Achieved 88.5% AUC score on unseen data
- Secured second place in Bird Audio Detection challenge
- Demonstrated effectiveness of combined CNN-RNN architecture

## Abstract

Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions. In this paper, we propose using convolutional recurrent neural networks on the task of automated bird audio detection in real-life environments. In the proposed method, convolutional layers extract high dimensional, local frequency shift invariant features, while recurrent layers capture longer term dependencies between the features extracted from short time frames. This method achieves 88.5% Area Under ROC Curve (AUC) score on the unseen evaluation data and obtains the second place in the Bird Audio Detection challenge.