## 3 Proposed Method
### Feature Extraction Based on the Improved MobileFaceNet
#### 3.2.2 Style-Based Recalibration Module  
Under different working conditions, the sounds measured at different sound source positions have different attribute characteristics, and these characteristics can be used for the classification and recognition of sounds. The sound data are transformed into spectrograms, and the local features of the images are extracted through the convolutional network layer [32]. However, for different sound spectrograms, there will be different style attributes, such as frequency and energy distribution. These style attributes will significantly affect the feature extraction process of the convolutional network and then have an impact on the subsequent recognition and classification. The SRM adaptively recalibrates these feature maps using the style of the intermediate feature maps. According to the style attributes of different images, the relative importance of each style feature is estimated dynamically, and the feature weights are adjusted accordingly, so that the network can extract image features more effectively. The structure of the SRM module is shown in Figure 7. C represents the number of channels; H and W \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Input** & **Convolution Operation** & **z** & **c** & **n** & **v** \\ \hline \(112\times 112\times 3\) & Conv \(3\times 3\) & & 64 & 1 & 2 \\ \(56\times 56\times 64\) & Depthwise Conv \(3\times 3\) & & 64 & 1 & 1 \\ \(56\times 56\times 64\) & bottleneck & 2 & 64 & 5 & 2 \\ \(28\times 28\times 64\) & bottleneck & 4 & 128 & 1 & 2 \\ \(14\times 14\times 128\) & bottleneck & 2 & 128 & 6 & 1 \\ \(7\times 7\times 128\) & bottleneck & 4 & 128 & 1 & 2 \\ \(7\times 7\times 128\) & bottleneck & 2 & 128 & 2 & 1 \\ \(7\times 7\times 128\) & Conv \(1\times 1\) & & 512 & 1 & 1 \\ \(7\times 7\times 512\) & Linear GDConv \(7\times 7\) & & 512 & 1 & 1 \\ \(1\times 1\times 512\) & Linear Conv \(1\times 1\) & & 128 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MobileFaceNet network structure. Figure 6: Framework diagram of STgram feature fusion. represent the height and width of the feature map, respectively; and \(d\) refers to the total number of style features. The SRM module is mainly composed of two components: style pooling and style integration. The style pooling layer obtains style information by calculating the channel-level statistics of the feature map, reflecting the style features of the feature map on a global scale. The style integration layer estimates the recalibration weight of each channel through the style integration operator and then adjusts the response intensity of the feature map. The style pooling layer consists of two parts, namely the average pooling layer and the standard deviation pooling layer. The input feature map \(X\in R^{N_{d}\times C\times H\times W}\), where \(N_{d}\) is the number of samples in the mini-batch, must first pass through the average pooling layer. The specific operation is as follows:  
\[\mu_{nc}=\frac{1}{\text{HW}}\sum_{h=1}^{\text{H}}\sum_{w=1}^{W}x_{nchw} \tag{5}\]  
In Equation (5), \(\mu_{nc}\) is the result of the average pooling of the original feature map; \(x_{nchw}\) is the original feature map; and \(n\), \(c\), \(h\), and \(w\) are the components of \(N_{d}\), C, H, and W, respectively. Secondly, to calculate the standard deviation of the result of the average pooling layer and the original input feature map, the specific process is as follows:  
\[\sigma_{nc}\,=\,\sqrt{\frac{1}{\text{HW}}\sum_{h=1}^{\text{H}}\sum_{w=1}^{W} \left(\,x_{nchw}\,-\mu_{nc}\right)^{2}} \tag{6}\]  
In Equation (6), \(\sigma_{nc}\) is the result after passing through the standard pooling layer. Finally, the result after passing through the style pooling layer is as follows:  
\[t_{nc}\,=\,[\mu_{nc},\,\sigma_{nc}\,] \tag{7}\]  
In Equation (7), \(t_{nc}\) is the feature vector after passing through the style pooling layer. The style integration layer consists of three parts, namely, Channel-wise Fully Connected (CFC), Batch Normalization (BN), and the activation layer.