# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 2 Related Work
### Acoustic Features  
With the growing research in fields like speech recognition, sentiment analysis, and anomaly monitoring, there is an increasing emphasis on the precise acquisition and comprehension of sound input features. The rapid advancements in raw wave feature extraction methods have laid a strong foundation for various practical applications based on sound signals. [PERSON] et al. [27] used the log-Mel spectrogram as an input feature to detect harmonic drive anomalies. The log-Mel spectrogram feature can better simulate the hearing characteristics of the human ear, and convert the linear interval on the frequency axis to the nonlinear interval on the Mel scale, which is more in line with the perception of the human ear and retains enough acoustic information. However, it might filter out high-frequency components of anomalous sound, where distinct features may be present. [PERSON] et al. [12] propose a multi-stage deep autoencoder network (DAN) that combines the information of three input features, an MFCC, Gabor filter bank, and bark filter bank, to detect abnormal sounds in traffic scenes. It identifies new potential insights through the nonlinear transformation and dimensionality reduction processes of the DAN, ultimately producing deep audio representations (DARs). The DAR integrates the complementary information of all input features through multiple-stage DANs. This approach integrates the strengths of input features, enhancing the overall representation. However, too many input features have been used, so calculating the potential insights of multiple features requires a tremendous amount of computation. [PERSON] et al. [28] propose a machine anomalous sound detection method based on a spectral-temporal information fusion model. To compensate for the lack of high-frequency information in the log-Mel spectrogram, the author suggests integrating the temporal feature (Tgram) extracted by the TgramNet network. This method effectively compensates for anomalous information that may be absent in the log-Mel spectrogram.  
However, in comparison to the log-Mel spectrogram, the performance of detection may be influenced by the noise present in the temporal information represented by Tgram features.  
In 2018, [PERSON] et al. [29] designed an architecture for extracting acoustic features called SincNet. The SincNet filters used in this architecture are the inverse Fourier transform of some rectangular band-pass filters, inspired by band-pass filters in the field of signal processing. [PERSON] et al. [30] extended this concept by designing a multi-scale SincNet (MS-SincNet) 2D representation extraction network based on the SincNet filter. The MS-SincNet network autonomously learns filter parameters and exhibits a degree of anti-noise ability. This underscores the capability of the SincNet spectrogram, generated by the MS-SincNet network, to effectively capture fine machine sound signal details that the log-Mel spectrogram might overlook. It can distinguish machine sounds from background noise, making it a valuable asset for the task of anomalous sound detection. Therefore, exploring the information representation ability of the SincNet spectrogram in anomalous sound detection is of great significance.