ekazakos / esc-two-stream Goto Github PK

This is a reimplementation of the paper: "Yu Su, Ke Zhang, Jingyu Wang, Kurosh Madani, Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion, Sensors 2019" link

For two stream fusion we simply average the predictions of LMC and MC networks.

The hyperparameters that we used for training are:

• learning rate: 0.001
• batch size: 32
• dropout: 0.5
• weight decay: 0.0005
• optimiser: Adam

To train the LMC model, run:

python train.py LMC --train_pickle path/to/UrbanSound8K_train.pkl --test_pickle path/to/UrbanSound8K_test.pkl 

To train MC annd MLMC models, simply replace the first argument in the line above with MC and MLMC respectively.

To test the LMC model, run:

python test.py LMC --weights_dir models/mode\=LMC/date/ --scores_output scores/LMC.pkl --test_pickle path/to/UrbanSound8K_test.pkl --mapping class_mapping.pkl --average

To test MC annd MLMC models, simply replace every instance of LMC in the line above with MC and MLMC respectively.

To test the 2 stream fusion model (assuming that you have already trained and tested LMC and MC), run:

python test.py LMC+MC --scores_input scores/LMC.pkl scores/MC.pkl --scores_output scores/LMC_MC_fusion.pkl --mapping class_mapping.pkl --average