# Pyramid Feature Attention Network for Speech Resampling Detection
## 3 Experiments and Analysis
### Results and Analysis
#### 3.2.3 Different Compression Sampling Factors  
In this section, our main objective is to compare the detection capabilities of the proposed model with traditional methods under different sampling factors. In traditional methods, [PERSON] et al. [16] calculated the second-order difference of the speech signal and found the periodicity of the zero-value interval to detect resampling. [PERSON] et al. [18] found that an incomplete interpolation process resulted in inconsistent bandwidth and sampling frequency of the spectrogram to detect resampling. [PERSON] et al. [19] used LBP operators to model time-frequency statistical changes in the spectrogram, and used SVM to classify the features of the LBP histogram to distinguish between original speech and resampled speech. In the case of large-scale speech tampering, the above methods are able to determine speech resampling to some extent. However, for small-scale tamper detection, that is, the resampling factor is 0.8 to 1.2, they do not show high detection accuracy. Therefore, the experiment in this section compares the detection accuracy of 10 resampling factors using the proposed model and other methods on the mixed resampling corpus, as shown in the following table.  
The results in Table 5 show that the detection accuracy of our proposed model is better than other methods under a resampling factor of 0.6\(\sim\)1.6. Moreover, the accuracy under resampling factors of 0.8, 0.9, 1.1, and 1.2, which are difficult to detect, is greatly improved. In particular, the accuracy of resampling-tampering detection with \(\mathrm{p/q}=0.9\) is increased by nearly 20%. And with an increase or decrease in the resampling factor, the accuracy rate gradually improves. The reason is that the larger the scale of down-sampling and up-sampling, the more obvious are the time-frequency variation characteristics displayed on the spectrogram. The model can effectively learn its changes and make better classification decisions, which verifies that the proposed model has a strong ability to extract resampling features from the spectrogram, and has high generalization.  
\begin{table}
\begin{tabular}{c c c c} \hline
**p/q** & **EfficientNet-B0** & **ViT** & **ResNet14** \\ \hline
0.8 & 0.9270 & 0.9533 & **0.9657** \\
0.9 & 0.8510 & 0.8867 & **0.8908** \\
1.1 & 0.9240 & 0.9397 & **0.9767** \\
1.2 & 0.9620 & 0.9826 & **0.9930** \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of detection results for different backbone networks.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**p/q** & **[PERSON] et al. [16]** & **[PERSON] et al. [18]** & **[PERSON] et al. [19]** & **The Proposed Model** \\ \hline
0.6 & 0.7120 & 0.6593 & 0.9813 & **0.9988** \\
0.7 & 0.6884 & 0.5573 & 0.9407 & **0.9916** \\
0.8 & 0.7061 & 0.4943 & 0.8553 & **0.9657** \\
0.9 & 0.6876 & 0.4490 & 0.6960 & **0.8908** \\
1.1 & 0.6902 & 0.6247 & 0.7413 & **0.9767** \\
1.2 & 0.6657 & 0.7027 & 0.8127 & **0.9930** \\
1.3 & 0.6603 & 0.7090 & 0.9093 & **0.9961** \\
1.4 & 0.6523 & 0.7617 & 0.9433 & **0.9971** \\
1.5 & 0.6346 & 0.7873 & 0.9649 & **0.9949** \\
1.6 & 0.6804 & 0.8373 & 0.9791 & **0.9986** \\ \hline \end{tabular}
\end{table}
Table 5: Detection results of different network models.