# Convolutional Recurrent Neural Networks for Bird Audio Detection

arXiv: https://arxiv.org/abs/1703.02317 · HTML: https://mlsanity.com/paper/1703.02317/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 5 of 5)

- [Detection of Audio-Video Synchronization Errors Via Event Detection](https://mlsanity.com/paper/2104.10116/) (2021)
- [SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification](https://mlsanity.com/paper/2103.01929/) (2021)
- [Automatic Bat Call Classification using Transformer Networks](https://mlsanity.com/paper/2309.11218/) (2023)
- [Towards Automated Animal Density Estimation with Acoustic Spatial Capture-Recapture](https://mlsanity.com/paper/2308.12859/) (2023)
- [Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection](https://mlsanity.com/paper/2110.06126/) (2021)

## Abstract (arXiv)

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.