Official code implementation of "MAD: A Military Audio Dataset for Situational Awareness and Surveillance"

Install the necessary packages with:

$ pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
$ pip install -r requirements.txt

Please first download mad_dataset_annotation.csv, train.csv and test.csv from figshare. The mad_dataset_annotation.csv must be located in ./ The training.csv and test.csv file must be located in ./data/MAD_dataset/

To download all the audio samples from youtube url.

python3 youtube_audio_download.py

It takes around 2-3 hours to download all the videos

We then extract waveforms using audio segmentation labels.

python get_sample.py

All the samples must be located in ./data/MAD_dataset/training and ./data/MAD_dataset/test

To simply train the model, run the shell files in scripts/.

1. scripts/military_resnet18_ce.sh: Cross-Entropy loss with ResNet18 (w/ pretrained weights on ImageNet) model.
2. scripts/military_resnet18_ce_scratch.sh: Cross-Entropy loss with ResNet18 (w/o pretrained weights on ImageNet --> from scratch training) model.
3. scripts/military_resnet50_ce.sh: Cross-Entropy loss with ResNet50 (w/ pretrained weights on ImageNet) model.
4. scripts/military_cnn6_ce.sh: Cross-Entropy loss with CNN6 (w/ pretrained weights on AudioSet) model.
5. scripts/military_efficient_b0_ce.sh: Cross-Entropy loss with EfficientNet-B0 (w/ pretrained weights on AudioSet) model.
6. scripts/military_ast_ce.sh: Cross-Entropy loss with AST model (w/ pretrained weigths on ImageNet & AudioSet).
7. scripts/military_patchmix_ce.sh: Cross-Entropy loss with AST model (w/ pretrained weigths on ImageNet & AudioSet), where the label depends on the interpolation ratio. Except for these scripts, there are many scripts in scripts/. Please check.

Important arguments for models.

• --model: network architecture, see models
• --from_sl_official: load ImageNet or AudioSet pretrained checkpoint
• --audioset_pretrained: load AudioSet pretrained checkpoint and only support AST

Important arugment for evaluation.

• --eval: switch mode to evaluation without any training
• --pretrained: load pretrained checkpoint and require pretrained_ckpt argument.
• --pretrained_ckpt: path for the pretrained checkpoint

The pretrained model checkpoints will be saved at save/[EXP_NAME]/best.pth.

• June-Woo Kim: kaen2891@knu.ac.kr