# Pyramid Feature Attention Network for Speech Resampling Detection
## 3 Experiments and Analysis
### Results and Analysis
#### 3.2.2 Architecture Comparison  
With the innovation of deep learning, the ability of neural networks to extract features is also constantly enriched. In this paper, one-dimensional speech signals are converted into a two- dimensional spectrogram, and neural networks are used to extract the spectrogram features, which greatly improves the ability of speech resampling detection. In this section, after attempting to train different network models, we construct a deep attention network that combines low-level location features with high-level semantic features and adds SE attention modules. In the experiment, classic classification networks are compared with the proposed model to test the accuracy of resampling detection. ResNet14 is a feature extractor formed by stacking four Bottlenecks, and FPN is a model structure that integrates top-down two-layer feature maps based on ResNet14. The speech of the four sampling factors in the table is from the mixed corpus. The data in Table 3 and Figure 6 show the impact of the four network models on the accuracy of different resampling factors. It is worth noting that when \(\text{p}/\text{q}=0.8\) and \(\text{p}/\text{q}=1.2\), AlexNet, ResNet14, and FPN all have good detection capabilities. When the resampling factor is 1.1, it is observed that the classification ability of the AlexNet model decreases, and the detection performance of ResNet14 also slightly weakens. When \(\text{p}/\text{q}=0.9\), the detection accuracy of the four models has a different degree of decline, among which AlexNet and ResNet14 decrease significantly, indicating that the two comparison models could not adapt to tamper detection of multiple resampling factors. The accuracy rate of the proposed model is as high as 89.09%, which is about three percentage points higher than the high-performance FPN. In addition, under other sampling factors, the proposed model achieved optimal results, which verifies its effectiveness. \begin{table}
\begin{tabular}{c c c c c} \hline
**p/q** & **Method** & **Mixed Interpolation** & **Linear Interpolation** & **Spline Interpolation** & **Cubic Interpolation** \\ \hline \multirow{3}{*}{0.8} & [PERSON] et al. [16] & 0.7061 & 0.7237 & 0.7061 & 0.6841 \\  & [PERSON] et al. [18] & 0.4943 & 0.4820 & 0.5310 & 0.4950 \\  & [PERSON] et al. [19] & 0.8553 & 0.8360 & 0.8340 & 0.8270 \\  & **Proposed Method** & **0.9657** & **0.9573** & **0.9610** & **0.9656** \\ \hline \multirow{3}{*}{0.9} & [PERSON] et al. [16] & 0.6876 & 0.6771 & 0.6619 & 0.6673 \\  & [PERSON] et al. [18] & 0.4490 & 0.4140 & 0.4840 & 0.4550 \\  & [PERSON] et al. [19] & 0.6960 & 0.7030 & 0.6600 & 0.6950 \\  & **Proposed Method** & **0.8908** & **0.8982** & **0.8883** & **0.8900** \\ \hline \multirow{3}{*}{1.1} & [PERSON] et al. [16] & 0.6902 & 0.6514 & 0.6806 & 0.6817 \\  & [PERSON] et al. [18] & 0.6247 & 0.6300 & 0.6690 & 0.6700 \\  & [PERSON] et al. [19] & 0.7413 & 0.7530 & 0.7440 & 0.7430 \\  & **Proposed Method** & **0.9767** & **0.9810** & **0.9910** & **0.9720** \\ \hline \multirow{3}{*}{1.2} & [PERSON] et al. [16] & 0.6657 & 0.6745 & 0.6201 & 0.6675 \\  & [PERSON] et al. [18] & 0.7020 & 0.6620 & 0.7490 & 0.7030 \\ \cline{1-1}  & [PERSON] et al. [19] & 0.8127 & 0.8160 & 0.8460 & 0.8000 \\ \cline{1-1}  & **Proposed Method** & **0.9930** & **0.9770** & **0.9986** & **0.9834** \\ \hline \end{tabular}
\end{table} 
Table 2: Resampling detection results under different interpolation methods. \begin{table}
\begin{tabular}{c c c c c} \hline
**p/q** & **AlexNet** & **ResNet14** & **FPN** & **The Proposed Model** \\ \hline
0.8 & 0.9495 & 0.9470 & 0.9595 & **0.9657** \\
0.9 & 0.5758 & 0.7650 & 0.8614 & **0.8908** \\
1.1 & 0.7504 & 0.9003 & 0.9664 & **0.9767** \\
1.2 & 0.9742 & 0.9714 & 0.9909 & **0.9930** \\ \hline \end{tabular}
\end{table} 
Table 3: Detection results of different network models. Figure 7 shows the training loss curves of different network models when the resampling factor is 1.1.