# Multi-Level Attention-Based Categorical Emotion Recognition Using Modulation-Filtered Cochleagram
## 3 Emotional Recognition Model
### Channel-Level Attention  
The channel-level attention module is used to calculate the channel-wise attention map from the 3D convolution feature map. This attention map helps to recalibrate the weights of each channel, allowing the model to focus on informative parts of the input. The design of the channel-level attention module is similar to that of the CBAM, with a slight difference. In this module, two additional 3D convolutional layers are inserted to capture spatial and temporal information from the feature maps specific to each channel. To compute the channel-level attention efficiently, the spatial and temporal dimensions of the input feature map are squeezed. This operation reduces the feature map's spatial and temporal dimensions while preserving the channel information. The resulting squeezed feature map is then passed through two separate 3D convolutional layers to extract spatial and temporal information for each channel. The outputs of these convolutional layers are used to compute the channel-level attention map. Figure 3 illustrates the structure of the channel-level attention module. The channel-level attention map is first obtained through adaptive learning, and then element-level multiplication with the input feature map \(F_{3D}\) is used to obtain a refined feature map \(F_{3D}^{\prime}\). The calculation formula is provided as follows:  
\[F_{3D}^{\prime}=C_{3D}(F_{3D})\bigotimes F_{3D}, \tag{8}\]  
where \(C_{3D}\) represents the channel-level attention map, with \(\bigotimes\) representing the element-level multiplication.  
We first aggregate spatial information of a feature map \(F_{3D}\) by using both average-pooling and max-pooling operations, generating two different spatial context descriptors: \(Avgpool(F_{3D})\) and \(Maxpool(F_{3D})\), which denote adaptive average-pooling features and max-pooling features, respectively.  
Figure 3: The channel-level attention module.  
Figure 2: Overview of multi-level attention-based emotion recognition model.  
Both descriptors are then fed into two 3D convolutional layers with a rectified linear unit (ReLU) function. Subsequently, the features are fused using element-wise summation, and the sigmoid activation function is applied to obtain the channel attention map \(C_{3D}\in R^{1\times 1\times 1\times C}\). The channel-level attention map indicates how important each channel is for the emotion recognition results. The calculation formula is as follows:  
\[C_{3D}(F_{3D})=\sigma(Conv_{2}(Relu(Conv_{1}(Maxpool(F_{3D}))))+Conv_{2}(Relu( Conv_{1}(Avgpool(F_{3D}))))), \tag{9}\]  
where \(Conv_{1}\) and \(Conv_{2}\) represent the first and second 3D convolution operations, respectively, and \(\sigma\) denotes a sigmoid operation. Both convolutions are \(1\times 1\times 1\) convolution kernels, the number of output channels is \(\frac{C}{r}\) and \(C\), and r is the dimensionality reduction coefficient in the channel-level attention, with a value of 16. The batch normalization after the channel feature map \(C_{3D}\) is used to obtain the same network input distribution and improve the effectiveness of different channels on the feature maps.