Table 13.
The VGGish-based architecture.
| Layer | Arguments | Number of Parameters |
|---|---|---|
| Convolution2D | Filters = 64, kernel size = (3, 3), strides = (1, 1), input shape (128, 170, 1) |
640 |
| MaxPooling2D | Pool size = (2, 2), strides = (2, 2) | |
| Convolution2D | Filters = 128, kernel size = (3, 3), strides = (1, 1) | 73,856 |
| MaxPooling2D | Pool size = (2, 2), strides = (2, 2) | |
| Convolution2D | Filters = 256, kernel size = (3, 3), strides = (1, 1) | 295,168 |
| Convolution2D | Filters = 256, kernel size = (3, 3), strides = (1, 1) | 590,080 |
| MaxPooling2D | Pool size = (2, 2), strides = (2, 2) | |
| Convolution2D | Filters = 512, kernel size = (3, 3), strides = (1, 1) | 1,180,160 |
| Convolution2D | Filters = 512, kernel size = (3, 3), strides = (1, 1) | 2,359,808 |
| MaxPooling2D | Pool size = (2, 2), strides = (2, 2) | |
| Flatten | ||
| Dense_1 | Nodes = 4096 | 184,553,472 |
| Dense_2 | Nodes = 4096 | 16,781,312 |
| Dense_3 | Nodes = 23 | 94,231 |
| Total number of parameters | 205,928,727 |