light-VGG11 is the lightweight version of VGG11, which is used for identifying chicken distress calls and more suitable for practical deployment.
This is my experiment environment:
- python3.7
- pytorch+cuda11.2
The dataset named 'Used_AudioAndLabels.pkl'(https://figshare.com/articles/dataset/Automated_identification_of_chicken_distress_vocalisations_using_deep_learning_models/20049722) contains 3,363 distress calls and 1,973 natural barn sounds with one second. Each second contains 22,050 time series points.
According to the original datasets, we should preprocess them and split them into three parts (training, validation, and test sets) based on fivefold cross-validation technique. The detailed procedures can be found in Section 2.3. Herein, the split five folds can find at the link "https://drive.google.com/drive/folders/1W8yypgILtBFo5307zk8shT3Mnps_m7Tq?usp=sharing". Each fold contains a training set, a validation set, and a test set. We can directly place them into our own local folder and change the path command "--data_path '..1/2/3/4/5_new_separate_normal_myTensor_Log_power_88_bird_acoustic.pt'" in the training_script.sh. Then, the model can be run normally.
The related paper titled "Automated identification of chicken distress vocalisations using deep learning models" is available at "https://royalsocietypublishing.org/doi/10.1098/rsif.2021.0921".