# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 1 Introduction
All three machines are highly accurate machines, so the main goal of this dataset is for highly accurate machine fault detection. In addition to that, the \"Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring\" task is introduced within the Detection and Classification of Acoustic Scenes and Events 2020 (DCASE 2020) [13], and has attracted the participation of numerous scholars worldwide. Figure 1: Unsupervised machine anomalous sound detection system framework. \(\Phi\) represents the threshold; when the anomaly score exceeds the \(\Phi\), the input is identified as anomalous. In real-world industrial environments, the working conditions of industrial machines are often highly complex. The operational sounds of these machines are frequently intertwined with a myriad of environmental noises. The sound signals produced by machines exhibit intricate characteristics, including nonlinearity and multiple aliasing [14]. Consequently, the extraction of effective acoustic features tailored to these characteristics plays a pivotal role in enhancing the performance of machine anomalous sound detection. A machine anomalous sound detection method that utilizes x-vectors as input features is proposed in [15]. The audio subnet of L3-Net was employed to extract audio embeddings. However, the network forces a distinct behavior of the x-vector, leading to performance degradation. [PERSON] et al. [16] adopt Gammatone filters for extracting acoustic information and generate the Gammatone spectrogram as input features to enhance the representation of machine sound amplitude and phase information. Nonetheless, the performance of Gammatone filters is closely linked to the configuration of calculation parameters, necessitating a substantial number of experiments to select suitable settings. In order to devise a more stable approach for extracting acoustic features from machines, some researchers have undertaken experimental investigations into the effectiveness of the log-Mel spectrogram feature [17; 18]. This feature extraction method is founded on the Mel filter bank, designed to emulate the auditory perception of the human ear. Consequently, the Mel filter bank exhibits a denser filter distribution in the low-frequency range and a sparser distribution in the high-frequency range. This indicates that the log-Mel spectrogram emphasizes the low-frequency information of machine sound, potentially leading to the loss of critical information during the acoustic feature extraction process. This may affect the feature expression of key machine sound information. In addition to exploring acoustic features, the selection of machine anomalous sound detection models is also a prominent topic within this field. [PERSON] et al. [19] employ the traditional interpolation deep neural network (IDNN) to learn the features of normal sounds by minimizing reconstruction errors. However, it may lead to misjudgments when there is some same information between normal and anomalous sound. [PERSON] et al. [20] introduce a flow-based machine anomalous sound detection method using normalizing flow for self-supervised density estimation. While this method improves detection performance, it necessitates the training of multiple detection models for each machine ID, limiting model generalization. With the rapid advancement of machine learning, many recent studies on machine anomalous sound detection have embraced deep learning methods. Among them, the lightweight network MobileNet series [21; 22; 23] has delivered exceptional results in tasks such as face recognition, image classification, and target detection. These MobileNet-based networks are characterized by a rapid computation speed, high flexibility, and lightness. The MobileNetV2 network [22] was first applied to anomalous sound detection in [24], achieving the top-ranking model in DCASE Task 2. However, the focus was primarily on improving the network's loss function and did not conduct in-depth research on the ability of the detection network in terms of feature extraction. [PERSON] et al.