Acoustic mosquito detection code with Bayesian Neural Networks

Last update: Nov 28, 2022

Overview

HumBugDB

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:
- Task 1: Mosquito Event Detection (MED)
- Task 2: Mosquito Species Classification (MSC)

General use instructions

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:

Code, feature, hyperparameter configuration

Installation instructions

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.

Google Colab

Installation and use with Google Colab here.

Jupyter notebook

Installation instructions and requirements for PyTorch: InstallationLogPyTorch.txt.
Keras min. requirements: condarequirementsKeras.txt and piprequirementsKeras.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/.

Contact

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.

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Releases(2.0)

2.0(Sep 1, 2021)
HumBugDB models

This release distributes the binaries for the Bayesian neural network models, to preserve the small size of the repository.

We include models trained for two tasks. The model objects and tasks are described at:

MED: Mosquito Event detection

MSC: Mosquito Species classification

The models correspond to all of the results reported in the paper HumBugDB: A Large-scale Acoustic Mosquito Dataset.
Source code(tar.gz)
Source code(zip)
MED_trained_models.zip(608.00 MB)
MSC_trained_models_1.zip(1280.73 MB)
MSC_trained_models_2.zip(1736.36 MB)
v1.0(Jun 9, 2021)
This release distributes the binaries for the Bayesian neural network models, to preserve the small size of the repository. We include five trained models, to be placed in /outputs/models/keras/ or /outputs/models/pytorch/:

PyTorch BNN-ResNet-50 (paper version): neurips_2021_humbugdb_resnet50_bnn.pth

BNN-ResNet-18 (paper version): neurips_2021_humbugdb_resnet18_bnn.pth

Keras BNN-4conv (paper version): neurips_2021_humbugdb_keras_bnn.hdf5

Keras BNN-4conv (best version): neurips_2021_humbugdb_keras_bnn_best.hdf5

Keras BNN-4conv (for appendix Test B): neurips_2021_humbugdb_supplement_TestB_keras_bnn.hdf5

Source code(tar.gz)
Source code(zip)
neurips_2021_humbugdb_keras_bnn.hdf5(6.00 MB)
neurips_2021_humbugdb_keras_bnn_best.hdf5(5.98 MB)
neurips_2021_humbugdb_resnet18_bnn.pth(42.71 MB)
neurips_2021_humbugdb_resnet50_bnn.pth(89.99 MB)
neurips_2021_humbugdb_supplement_TestB_keras_bnn.hdf5(5.98 MB)

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