# Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data

arXiv: https://arxiv.org/abs/2406.13579 · HTML: https://mlsanity.com/paper/2406.13579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 19 Jun 2024.

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

- [SSAST: Self-Supervised Audio Spectrogram Transformer](https://mlsanity.com/paper/2110.09784/) (2021)
- [Seeing biodiversity: perspectives in machine learning for wildlife conservation](https://mlsanity.com/paper/2110.12951/) (2021)
- [Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks](https://mlsanity.com/paper/1807.00129/) (2018)
- [Sound Event Detection: A Tutorial](https://mlsanity.com/paper/2107.05463/) (2021)
- [Global birdsong embeddings enable superior transfer learning for bioacoustic classification](https://mlsanity.com/paper/2307.06292/) (2023)
- [Automatic tagging using deep convolutional neural networks](https://mlsanity.com/paper/1606.00298/) (2016)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)
- [Audio Tagging by Cross Filtering Noisy Labels](https://mlsanity.com/paper/2007.08165/) (2020)
- [Motif Mining and Unsupervised Representation Learning for BirdCLEF 2022](https://mlsanity.com/paper/2206.04805/) (2022)
- [Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data](https://mlsanity.com/paper/2304.09657/) (2023)

## Built on by (most cited first, 1 of 1)

- [An Automated Pipeline for Few-Shot Bird Call Classification: A Case Study with the Tooth-Billed Pigeon](https://mlsanity.com/paper/2504.16276/) (2025)

## Abstract (arXiv)

Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects.