# A Machine Anomalous Sound Detection Method Using the IMS Spectrogram and ES-MobileNetV3 Network
## 1 Introduction  
With the rapid development of industrial technology and the continuous innovation of automation technology, modern industrial machine production equipment is rapidly evolving towards integration, automation, and intelligence. The stable operation of the production machine plays a critical role in ensuring production efficiency, product quality, and even the life and property safety of the employees. Therefore, it is imperative to monitor the working conditions of a machine during the process of machine operation to ensure the normal production of enterprises. The anomalous sound detection (ASD) system, which relies on the perception, processing, and decision making of sound signals, is garnering increasing enthusiasm in the field of monitoring, owing to its advantages in safeguarding user privacy [1], real-time capabilities [2], and adaptability to complex environments [3]. Currently, anomalous sound detection technology finds widespread application in various fields, including animal husbandry [4, 5], machine condition monitoring [6, 7], medical surveillance [8, 9], and many other aspects. In industrial scenarios, ASD involves a sound detection task for determining whether the sound emitted by a specific object is normal or anomalous. The detection methodscan be broadly categorized as supervised or unsupervised: supervised anomalous sound detection entails predefined anomalies during training, whereas unsupervised methods for anomalous sound detection provide solely normal sound samples for model training. However, in real-world factories, actual machine anomalous sounds are infrequent. Access is more readily available to sounds produced by machines operating under normal conditions, while machine anomalous sound is rare and highly diverse. To collect sufficient anomalous sound data, many machines and devices must be deliberately destroyed to record machine anomalous sounds. Deliberately damaging the machine to collect anomalous sound data of the machine requires very large machine purchase costs and human resources. Therefore, the collection of anomalous sound data through deliberate destruction is costly, making it challenging to collect a sufficient quantity of machine anomalous sound data for model training [10]. Therefore, the unsupervised anomalous sound detection method, which maximizes the use of normal sound data from the machine, is better suited to cope with the requirements of machine condition monitoring in real-world industrial scenarios. The general framework for unsupervised ASD is depicted in Figure 1. In unsupervised ASD, only normal sound samples are provided for model training. We first need to extract acoustic features from the raw sound data as inputs for the model. During the training process, the model will learn the sound information of the input features and calculate the anomalous score. Then, the anomalous score is calculated as the negative log-likelihood probability. During the model testing process, we need to set a threshold in advance. When the anomalous score of the input audio sample is less than the threshold, it will be judged as normal; otherwise, it will be judged as anomalous. As there is a considerable demand for anomalous sound monitoring in industrial application scenarios, some research teams have produced datasets on machine anomalous sound. We present a brief summary of the widely used machine anomalous sound dataset. The first dataset is the Case Western Reserve University (CWRU) Bearing Dataset [11]. It is specifically designed for studying the health condition of rolling element bearings in rotating machinery. The dataset may cover different types of bearings commonly found in industrial machinery. This diversity allows researchers to address the challenges associated with various bearing designs and operating conditions. The second common dataset is the mechanical dataset [12], including induction motors, gearboxes, and bearings with sizes of 6000, 9000, and 5000 time-series samples, respectively.