1.1 Index Terms Channel-Attention, U-Net, Complex Ratio Masking, Multichannel Speech Enhancement, Beamforming. 1.2 Related works A ratio/binary mask that acheives great performance in the single-channel setting. 这些框架在多通道环境下的性能滞后,因为大多数方法a)未能充分利用可用的空间信息;b)仍然将深...
Linear(channel, channel // reduction_ratio, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction_ratio, channel, bias=False), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size() y = self.gap(x).view(b, c) y = self.mlp(y).view(b, c, 1, 1)...
The proposed network is trained using 46,400 CT pictures as the training set and divides 11,600 CT images from the dataset at a ratio of 0.2 as the validation set. Finally, 7,250 CT images were used as the test set to test the performance of the network. The experimental results show...
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performan...
超参数调整:对于SENet等模块中的超参数(如降维比例ratio),需要进行适当的调整以找到最优的配置。 数据敏感性:channel attention模块的性能可能受到输入数据特性的影响,因此需要在实际应用中进行充分的验证。 集成方式:将channel attention模块集成到现有网络架构中时,需要考虑其与其他层或模块的连接方式,以确保整体网络的...
reduced_channels = max(1, round(input_channels / reduction_ratio)); % Initialize weights and biases layer.Weights1 = randn([1, 1, input_channels, reduced_channels],'single'); layer.Bias1 = zeros([1, 1, reduced_channels],'single'); ...
compress(x)x_out = self.spatial(x_compress)scale = torch.sigmoid_(x_out)return x * scaleclass TripletAttention(nn.Module):def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False):super(TripletAttention, self).__init__()self.ChannelGateH ...
Peak signal-to-noise ratio RMSE: Root-mean-square error SSIM: structural similarity index sCT: synthetic CT GT: Ground truth NPC: Nasopharyngeal carcinoma VMAT: Volumetric-modulated arc radiotherapy TV: Targets volume H&N: Head and neck RT: Radiotherapy PET: Positron emission tomogr...
DCT has a strong energy compaction property [1, 30], so it could achieve high data compression ratio with high quality [2, 22]. This property meets the demand of the chan- nel attention that representing a channel with a scalar. 2) DCT can be implemented with an element-wise multiplica...
To reduce parameter overhead, the hidden activation size is set to $\mathbb{R}^{C/r×1×1}$, where $r$ is the reduction ratio. After the shared network is applied to each descriptor, we merge the output feature vectors using element-wise summation. In short, the channel attention is ...