## 4 Experiments and Results  
### Data  
We first train and evaluate the model on two public datasets. The MIMI dataset [33] focuses on abnormal sound detection of industrial machines in real factory environments, including four types of equipment, valves, pumps, fans, and sliders, each with seven different models of machines. The dataset contains 26,092 normal state sounds and 6065 abnormal state sounds, with a sampling rate of 16 kHz, recorded using an eight-channel circular microphone array, and the microphone is 50 cm away from the machine (10 cm for valves). Abnormal types include actual faults such as valve contamination, water pump leakage, fan imbalance, and rail damage. The dataset is specially designed with noise mixed versions with different SNRs (6 dB, 0 dB, \(-\)6 dB), and the reality is enhanced by superimposing real factory background noise. The data are stored in segments that are 10 s long. The second one, ToyADMOS dataset [34], is designed for machine operation sound anomaly detection (ADMOS), and it includes three sub-datasets, toy cars, toy conveyor belts, and toy trains, corresponding to product quality inspection, fixed machinery fault diagnosis, and mobile machinery fault diagnosis tasks, respectively. The dataset collects abnormal sounds by artificially damaging micro-machine parts. Each sub-dataset contains more than 180 h of normal operation sounds and more than 4000 abnormal samples, with a sampling rate of 48 kHz and recorded synchronously with four microphones. Specifically, the toy car subset contains four different model combinations (motor + bearing), each model records 1350 11 s normal samples (66 h in total) and 250 abnormal samples, and the abnormal types include 53 fault modes such as shaft bending and gear deformation; the toy conveyor belt subset covers three models, each model has 1800 10 s normal samples (60 h) and 355 abnormal samples, involving 60 faults such as belt tension abnormality; and the toy train subset contains four models, each model has 1350 11 s normal samples (66 h) and 270 abnormal samples, covering 54 faults such as track damage. The pumps, fans, valves, and sliders in the dataset have distinct acoustic characteristics due to their designs. To mitigate the risk of conflating sources of variation, we trained dedicated models for each equipment type. This prevents cross-equipment interference and ensures that latent representations capture device-specific anomalies. In our experiments, all audio samples are resampled to 16 kHz and normalized to 10 s, and we only use the normal audio samples to ensure the unsurpervised training setup. In addition, we select six types of equipment in these datasets in our experiment and split them into training data and testing data based on a previous study [35], whose specific data distributions are shown in Figure 5.  
Figure 5: The distributions of training and testing data. Furthermore, this study also used the audio recorded from the wind turbine equipment in real world to test and compare the proposed model with the baseline models. This part of the data was collected in our previous work [36] using three recording devices, each of which is described below. The SS-ALIM652M1-002 voice algorithm motherboard is a six-microphone array motherboard product developed by SureSense Technology, Chongqing, China. The product is connected to AC108 externally, and the output format is a six-channel PCM signal with a sampling rate of 16 kHz and a depth of 16 bits. The device is shown in Figure 6.  
The Zoom H1n recording device (Zoom, San Jose, CA, USA ) uses a stereo X/Y microphone configuration, supports 24 bit/96 kHz audio signals, has a low-cut filter, can reduce unwanted low-frequency noise such as popping sound and wind noise, improves the recording quality, and can use a compressor to obtain a maximum sound pressure of up to 120 dB SPL to ensure that the recorded audio is not distorted.