This was a course project for Audio Content Analysis taught by Prof. Bello. It is based on a Kaggle competition to classify one-second long English speech commands.
Three models were implemented in Keras:
- a ConvNet
- a DenseNet
- a Dense-Recurrent Net
The Kaggle dataset was transformed with simple Gaussian noise to make a noisy variant of the clean dataset. The following table lists the multi-class accuracies of the three models on both clean and noisy datasets:
| Architecture | Architecture | ConvNet | DenseNet | Dense-Recurrent Net |
|---|---|---|---|---|
| Clean dataset | Training accuracy | 85.68% | 90.28% | 99.71% |
| Validation accuracy | 80.47% | 84.74% | 83.45% | |
| Testing accuracy | 81.49% | 85.04% | 83.39% | |
| Noisy dataset | Training accuracy | 88.02% | 88.67% | 99.59% |
| Validation accuracy | 83.64% | 81.95% | 82.74% | |
| Testing accuracy | 84.14% | 83.20% | 82.60% |
A copy of the report is available for reference.