# Automatic Bat Call Classification using Transformer Networks

arXiv: https://arxiv.org/abs/2309.11218 · HTML: https://mlsanity.com/paper/2309.11218/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference](https://mlsanity.com/paper/2104.01136/) (2021)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation](https://mlsanity.com/paper/2207.03450/) (2022)
- [Convolutional Recurrent Neural Networks for Bird Audio Detection](https://mlsanity.com/paper/1703.02317/) (2017)
- [Stacked Convolutional and Recurrent Neural Networks for Bird Audio Detection](https://mlsanity.com/paper/1706.02047/) (2017)

## Built on by (most cited first, 3 of 3)

- [bacpipe: a Python package to make bioacoustic deep learning models accessible](https://mlsanity.com/paper/2604.11560/) (2026)
- [Unlabeled Echoes: Pseudo-Labels and Genus-Aware Smoothing for Bat Call Recognition](https://mlsanity.com/paper/2609.11986/) (2026)
- [Effects of interpulse-interval variation on deep-learning classification of bat vocalizations](https://mlsanity.com/paper/2610.02284/) (2026)

## Abstract (arXiv)

Automatically identifying bat species from their echolocation calls is a difficult but important task for monitoring bats and the ecosystem they live in. Major challenges in automatic bat call identification are high call variability, similarities between species, interfering calls and lack of annotated data. Many currently available models suffer from relatively poor performance on real-life data due to being trained on single call datasets and, moreover, are often too slow for real-time classification. Here, we propose a Transformer architecture for multi-label classification with potential applications in real-time classification scenarios. We train our model on synthetically generated multi-species recordings by merging multiple bats calls into a single recording with multiple simultaneous calls. Our approach achieves a single species accuracy of 88.92% (F1-score of 84.23%) and a multi species macro F1-score of 74.40% on our test set. In comparison to three other tools on the independent and publicly available dataset ChiroVox, our model achieves at least 25.82% better accuracy for single species classification and at least 6.9% better macro F1-score for multi species classification.