# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 5 Conclusions  
This paper proposes a machine anomalous sound detection method using the IMS spectrogram and ES-MobileNetV3 network. The proposed method involves the extraction of both the log-Mel spectrogram and the SincNet spectrogram from the raw wave, which are concatenated to form a more discriminative machine sound feature IMS spectrogram. This feature is capable of representing a broader range of machine sound information  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Parameters (M)** & **FLOPs (M)** & **Accuracy** \\ \hline MobileNetV2 & 3.50 & 530.10 & 88.67 \\ MobileNetV3 & 1.80 & 417.36 & 92.14 \\ ES-MobileNetV3 & 1.53 & 421.37 & 96.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the evaluation indicators for the different network.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Slider** & **Valve** & **Pump** & **Fan** & **ToyCar** & **ToyConveyor** & **Average** \\ \hline  & **AUC (pAUC)** & **AUC (pAUC)** & **AUC (pAUC)** & **AUC (pAUC)** & **AUC (pAUC)** & **AUC (pAUC)** \\ \hline Baseline [13] & 84.76 (66.53) & 66.28 (50.98) & 72.89 (59.99) & 65.83 (52.45) & 78.77 (67.58) & 72.53 (60.43) & 73.51 (59.66) \\ x-vector [15] & 95.71 (79.45) & 94.87 (83.58) & 93.19 (81.10) & 97.35 (80.68) & 94.06 (86.80) & 84.22 (69.12) & 92.63 (80.12) \\ log-Mel [17] & 90.13 (73.97) & 84.87 (61.38) & 85.97 (71.10) & 80.06 (58.61) & 82.52 (66.34) & 76.75 (55.65) & 83.38 (64.51) \\ SI-gram [28] & 99.55 (97.61) & **99.64 (98.44)** & 91.94 (81.75) & 94.04 (88.97) & 94.48 (87.68) & 74.57 (63.60) & 92.36 (86.34) \\ IDNN [19] & 86.45 (67.58) & 84.09 (64.94) & 73.76 (61.70) & 67.71 (82.90) & 78.69 (69.22) & 71.07 (59.70) & 76.96 (62.57) \\ MobileNetV2 [24] & 95.27 (85.22) & 88.65 (87.98) & 82.53 (76.50) & 80.19 (74.40) & 87.66 (85.92) & 69.71 (56.43) & 84.34 (77.74) \\ Flow [20] & 94.60 (82.80) & 91.40 (75.00) & 83.40 (73.80) & 74.90 (65.30) & 92.20 (84.10) & 71.50 (59.00) & 85.20 (73.90) \\ Classification [32] & **99.97 (99.83)** & 95.82 (93.58) & 97.35 (91.58) & **99.96 (99.84)** & 92.02 (88.50) & 89.80 (80.61) & 95.82 (92.32) \\ Our Method & 99.42 (98.67) & 98.13 (96.28) & **97.64 (93.36)** & 97.78 (94.31) & **95.54 (99.54)** & **91.49 (82.13)** & **96.67 (92.38)** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different detection methods. The bold font indicates the best performance.  
and enhancing the distinctive characteristics of machine sound data. Additionally, an ES attention module is proposed to optimize the linear bottleneck structure of the MobileNetV3 network, which avoids the problem of information loss caused by the dimensionality reduction operation and retains richer feature information. The effectiveness of this method is validated through ablation experiments, demonstrating remarkable results on the DCASE 2020 Task 2 dataset.  
However, it should be acknowledged that this approach may not yield the best results for every machine type. In future work, our goal is to explore multi-modal information fusion methods that are well-suited for a broader range of machines, ultimately enhancing the generalization and robustness of the machine anomalous sound detection system.  
Conceptualization, M.W., X.S., and Q.M.; software, Q.M., M.W., and X.L.; experiment analysis, Q.M., F.Y., and J.X.; writing--review and editing, Q.M., X.S., and R.K.; funding acquisition, M.W., and H.Q. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (61961010 and 62071135); Projects from the Key Laboratory of Cognitive Radio and Information Processing, the Ministry of Education, and Guilin University of Electronic Technology (CRKL220105, CRKL20011, and CRKL220204); and the 'Ba Gui Scholars' program of the provincial government of Guangxi.  
Not applicable.