Research Article
Acoustic Scene Classification and Visualization of Beehive Sounds Using Machine Learning Algorithms and Grad-CAM
Table 2
Performance comparison of different features using various machine learning algorithms.
| Model | Features | Parameters | Accuracy (%) |
| SVM | Mel spectrogram | C = 0.01, gamma = 0.1 | 77.58 | MFCCs | C = 10, gamma = 0.01 | 85.63 | CQT | C = 0.1, gamma = 0.1 | 77.55 |
| Random Forest | Mel spectrogram | n_estimators = 100 | 82.14 | max_depth = 6 | min_samples_leaf = 3 | MFCCs | n_estimators = 100 | 86.82 | max_depth = 8 | min_samples_leaf = 5 | CQT | n_estimators = 200 | 74.07 | max_depth = 12 | min_samples_leaf = 3 |
| XGBoost | Mel spectrogram | n_estimators = 200 | 82.98 | learning_rate = 0.01 | max_depth = 4 | MFCCs | n_estimators = 800 | 87.36 | learning_rate = 0.01 | max_depth = 8 | CQT | n_estimators = 800 | 74.35 | learning_rate = 0.2 | max_depth = 20 |
|
|