Acoustic mosquito detection with Bayesian Neural Networks.
- Extract audio or features from our large-scale dataset on Zenodo.
- This repository outlines two key example use cases for the data:
This code is complementary to the paper: "HumBugDB: a large-scale acoustic mosquito dataset" and the dataset on Zenodo.
See documentation in the paper supplement for:
- Section A: Licensing
- Section B: Code use, feature and model engineering
- Section C: Description and visualisations of metadata in
data/metadata/*.csv
Additional documentation for:
You may choose to use the Colab environment which is natively compatible with all of our code. Alternatively, see the instructions for manually configuring an environment to run the Jupyter notebooks.
- Installation and use with Google Colab here.
- Installation instructions and requirements for PyTorch:
InstallationLogPyTorch.txt. - Keras min. requirements:
condarequirementsKeras.txtandpiprequirementsKeras.txt. Compatible with Tensorflow 1.X and 2.X.
After installation of requirements:
-
git clone https://github.com/HumBug-Mosquito/HumBugDB.git - Extract audio from four-part-archive to Zenodo to
/data/audio/.
Developed by Ivan Kiskin of MLRG University of Oxford. Contact [email protected] for enquiries or suggestions.
Follow our Twitter on @OxHumBug and visit our HumBug website for updates on the overall HumBug project.