# Pyramid Feature Attention Network for Speech Resampling Detection
## 3 Experiments and Analysis
### Results and Analysis
#### 3.2.4 The Impact of MP3 Compression  
Speech compression is necessary in the process of voice transmission. In real-world scenarios, MP3 is the most commonly used speech format, which is obtained by compressing WAV speech [41]. However, the MP3 compression operation may obscure previous resampling traces in speech, so we test the robustness of MP3 compression in this section.  
The experiment uses compression bit rates of 128 kbps, 64 kbps, and 32 kbps to compress speech signals, as shown in Figure 8. As the compression bit rate decreases, the frequency resolution of the high-frequency region in the spectrogram decreases, resulting in a more ambiguous representation of high-frequency signals and a relatively clearer representation of low-frequency signals [42]. A low compression bit rate leads to distortion of the speech signal, which makes abnormal spectral components appear in the spectrogram.  
In order to analyze whether the changes in the spectrogram caused by MP3 compression has some influence on the resampling detection model in this paper, we train and test the original speech and resampled speech with different compression bit rates. The experiments analyze the changes in the model detection effect after the original speech and the resampled speech of each factor are compressed, as shown in Table 6.  
It can be observed from Table 6 and Figure 9 that the performance of the model is disturbed after MP3 compression, and the detection performance of the algorithm is degraded to some extent, but the proposed method still has a high detection accuracy for compressed resampled speech. By observing the feature map captured by the model, it is found that the smaller the speech compression bit rate, the more significant the distortion  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Bit Rate** & **Method** & **0.6** & **0.7** & **0.8** & **0.9** & **1.1** & **1.2** & **1.3** & **1.4** & **1.5** & **1.6** \\ \hline \multirow{4}{*}{32 kbps} & [PERSON] et al. [16] & 0.6984 & 0.6977 & 0.6968 & 0.7222 & 0.7303 & 0.7485 & 0.6549 & 0.  
and noise effects of the signal, but the classification ability of the model is improved. This is because the sampling rate of the original speech does not change before compression, and the degree of information loss is relatively light, while the resampled speech being compressed results in more information loss due to the sampling rate conversion before compression, so that the difference between the two is more significant.  
Figure 9: Resampling detection results with different compression bit rates: (**a**) 32 kbps, (**b**) 64 kbps, and (**c**) 128 kbps [16; 18; 19].  
However, at a compression bit rate of 32 kbps, two types of resampled speech close to the original speech have a lower detection capability than the method of [PERSON] et al. [19] because the LBP operator is a local feature descriptor, which has a better representation of high-frequency detail loss, and the proposed model focuses on learning more complex global time-frequency variation features. Compared with other methods, for which the resampling detection performance degrades at high compression bit rates, the detection accuracy of the proposed model is less affected by the degree of compression, indicating that the proposed method is strongly resistant to compression.