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Architecture and parameters of the CRNN model
| Layer | Number of filters | Kernel size | Stride | Output shape | Number of parameters | Regularization |
|---|---|---|---|---|---|---|
|
| ||||||
| Input | - | - | - | (1000, 27) | 0 | - |
| 1D convolution | 32 | 3 | 1 | (998, 32) | 2624 | - |
| Max-pooling | 32 | 3 | 2 | (499, 32) | 0 | Dropout (0.5) |
| 1D convolution | 64 | 3 | 1 | (497, 64) | 6208 | - |
| Max-pooling | 64 | 3 | 2 | (248, 64) | 0 | Dropout (0.5) |
| GRU | 35 | - | - | (248, 35) | 10500 | - |
| Time distributed | 35 | - | - | (248, 35) | 1260 | - |
| Global average pooling | 35 | - | - | (1, 35) | 0 | - |
| Dense | 35 | - | - | (1, 2) | 72 | - |
| Total | 20664 | |||||
