An example transfer learning approach to cough audio data classification using transfer learning

This example is the part of the below work. Please cite if you find it useful:

Akgun, D., et al. "A transfer learning-based deep learning approach for automated COVID-19diagnosis with audio data." Turkish Journal of Electrical Engineering and Computer Sciences 29.8 (2021): 2807-2823

@Article{akgun2021transfer,
title={A transfer learning-based deep learning approach for automated COVID-19diagnosis with audio data},
author={AKG{"U}N, DEVR{.I}M and KABAKU{\c{S}}, ABDULLAH TALHA and {\c{S}}ENT{"U}RK, ZEHRA KARAPINAR and {\c{S}}ENT{"U}RK, ARAFAT and K{"U}{\c{C}}{"U}KK{"U}LAHLI, ENVER},
journal={Turkish Journal of Electrical Engineering and Computer Sciences},
volume={29},
number={8},
pages={2807--2823},
year={2021}
}

Converting audio data to image using Melspectogram:

Training system:

Example results

Batch size= 4 Learning rate= 0.005 Acc= [0.8695652 0.8913044 0.82417583 0.84615386 0.83516484] Average= 0.8532728 num_iters= 184

Batch size= 4 Learning rate= 0.001 Acc= [0.8695652 0.9130435 0.82417583 0.83516484 0.82417583] Average= 0.85322505 num_iters= 149

Batch size= 4 Learning rate= 0.0005 Acc= [0.8804348 0.90217394 0.84615386 0.85714287 0.82417583] Average= 0.8620163 num_iters= 109

Batch size= 4 Learning rate= 0.0001 Acc= [0.8804348 0.9130435 0.83516484 0.82417583 0.84615386] Average= 0.8597945 num_iters= 146

Batch size= 8 Learning rate= 0.005 Acc= [0.8863636 0.90909094 0.84090906 0.85227275 0.8863636 ] Average= 0.875 num_iters= 300

Batch size= 8 Learning rate= 0.001 Acc= [0.875 0.89772725 0.82954544 0.82954544 0.85227275] Average= 0.8568182 num_iters= 187

Batch size= 8 Learning rate= 0.0005 Acc= [0.89772725 0.90909094 0.84090906 0.84090906 0.85227275] Average= 0.8681818 num_iters= 224

Batch size= 8 Learning rate= 0.0001 Acc= [0.89772725 0.89772725 0.85227275 0.85227275 0.8636364 ] Average= 0.8727273 num_iters= 110

Batch size= 16 Learning rate= 0.005 Acc= [0.8875 0.9125 0.85 0.8625 0.875 ] Average= 0.87749994 num_iters= 295

Batch size= 16 Learning rate= 0.001 Acc= [0.8875 0.9125 0.85 0.8375 0.8375] Average= 0.86500007 num_iters= 158

Batch size= 16 Learning rate= 0.0005 Acc= [0.8625 0.925 0.8625 0.85 0.8375] Average= 0.8675 num_iters= 126

Batch size= 16 Learning rate= 0.0001 Acc= [0.9 0.925 0.85 0.85 0.8625] Average= 0.87750006 num_iters= 227

Batch size= 32 Learning rate= 0.005 Acc= [0.90625 0.90625 0.890625 0.875 0.859375] Average= 0.8875 num_iters= 160

Batch size= 32 Learning rate= 0.001 Acc= [0.890625 0.9375 0.875 0.859375 0.859375] Average= 0.884375 num_iters= 176

Batch size= 32 Learning rate= 0.0005 Acc= [0.875 0.9375 0.875 0.875 0.890625] Average= 0.890625 num_iters= 184

Batch size= 32 Learning rate= 0.0001 Acc= [0.90625 0.9375 0.875 0.84375 0.875 ] Average= 0.8875 num_iters= 139

Batch size= 64 Learning rate= 0.005 Acc= [0.875 0.921875 0.90625 0.859375 0.890625] Average= 0.890625 num_iters= 205

Batch size= 64 Learning rate= 0.001 Acc= [0.90625 0.90625 0.90625 0.828125 0.890625] Average= 0.8875 num_iters= 218

Batch size= 64 Learning rate= 0.0005 Acc= [0.90625 0.921875 0.90625 0.828125 0.859375] Average= 0.884375 num_iters= 208

Batch size= 64 Learning rate= 0.0001 Acc= [0.890625 0.9375 0.890625 0.875 0.890625] Average= 0.896875 num_iters= 300