# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 4 Experiment and Analysis
### Comparison of Different Input Features  
To investigate the impact of different input features on detection performance and validate the effectiveness of the IMS spectrogram, we conduct ablation experiments using the log-Mel spectrogram, SincNet spectrogram, and IMS spectrogram as input features for the MobileNetV3 network. The experimental results are shown in Figure 8. Analyzing the results, we observe that when the log-Mel spectrogram is employed as the input feature, the AUC and pAUC in the machine types Slider, ToyCar, and ToyConveyor are higher than those with the SincNet spectrogram as the input feature. However, the AUC and pAUC for the machine types Valve, Pump, and Fan are higher when using the SincNet spectrogram as the input feature. This suggests that the log-Mel spectrogram and the SincNet spectrogram are complementary in the feature extraction of machine acoustic signals. Notably, when the IMS spectrogram is employed as the input feature, there are significant improvements in AUC and pAUC for all machine types, particularly for Valve and Fan, where AUC and pAUC improve by 6.99% and 9.58%, respectively, compared to using the log-Mel spectrogram as the input feature alone. However, the improvement for ToyConveyor is less pronounced. This may be due to the high similarity of normal audio data in ToyConveyor and the introduction of highly repetitive information due to multi-feature input, leading to overfitting during training.  
Figure 8: Performance comparison for different input features. (**a**) AUC. (**b**) pAUC.  
To vividly illustrate the complementarity of the two acoustic feature signal extraction methods, we use the example of Fan and provide t-distribution stochastic neighbor embedding (t-SNE) cluster visualizations of the latent features extracted from the log-Mel spectrogram, SincNet spectrogram, and IMS spectrogram in Figure 9. In Figure 9a,b, when using the log-Mel spectrogram as the input feature, the normal and anomalous samples of ID00 overlap, while the normal and anomalous samples of ID02 are well distinguished. In contrast, in Figure 9c,d, where the SincNet spectrogram is the input feature, the normal and anomalous samples of ID00 can be well distinguished, but the normal and anomalous samples of ID02 overlap. However, in Figure 9e,f, both the normal and anomalous samples of ID00 and ID02 can be effectively differentiated. This demonstrates that the SincNet spectrogram complements the feature information filtered by the log-Mel spectrogram, while the IMS spectrogram provides greater discrimination capabilities.  
Figure 9: The t-SNE visualization of latent embeddings for test data of the machine type Fan. (**a**) log-Mel spectrogram (ID00), (**b**) log-Mel spectrogram (ID02), (**c**) SincNet spectrogram (ID00), (**d**) SincNet spectrogram (ID02), (**e**) IMS spectrogram (ID00), and (**f**) IMS spectrogram (ID02), respectively, show the visualization results of ID00 and ID02 in the test data when using log-Mel spectrogram, SincNet spectrogram, and IMS spectrogram, respectively. The “**a**” and “\(\times\)” denote normal and anomalous classes, respectively.