return ResNet(BasicBlock, [2, 2, 2, 2]) def ResNet34(): return ResNet(BasicBlock, [3, 4, 6, 3]) def ResNet50(): return ResNet(Bottleneck, [3, 4, 6, 3]) def ResNet101(): return ResNet(Bottleneck, [3, 4, 23, 3]) def ResNet152(): return ResNet(Bottleneck, [3, ...
The SE-ResNet50 audio classification model proposed in this paper is implemented by introducing the SE attention mechanism based on the ResNet-50 model, which is a commonly used classification model in the visual domain [22]. ResNet-50 is also used as the baseline model set in this ...