# Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection

arXiv: https://arxiv.org/abs/1706.02047 · HTML: https://mlsanity.com/paper/1706.02047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2017.

## Built on by (most cited first, 2 of 2)

- [Automatic Bat Call Classification using Transformer Networks](https://mlsanity.com/paper/2309.11218/) (2023)
- [Towards Lexical Analysis of Dog Vocalizations via Online Videos](https://mlsanity.com/paper/2309.13086/) (2023)

## Abstract (arXiv)

This paper studies the detection of bird calls in audio segments using stacked convolutional and recurrent neural networks. Data augmentation by blocks mixing and domain adaptation using a novel method of test mixing are proposed and evaluated in regard to making the method robust to unseen data. The contributions of two kinds of acoustic features (dominant frequency and log mel-band energy) and their combinations are studied in the context of bird audio detection. Our best achieved AUC measure on five cross-validations of the development data is 95.5% and 88.1% on the unseen evaluation data.