Variational Deep Embedding for Unsupervised Clustering of Industrial Noise in Steelmaking Plants

Industrial plants are major sources of environmental noise, producing complex and high-intensity acoustic emissions that vary across different operational conditions. Automatically characterizing the sources that generate harmful acoustic emissions is crucial to take the necessary actions to reduce them. However, manually labeling these sounds is impractical due to their volume and variability. In this study, we employ an unsupervised deep learning framework for clustering industrial sound emissions in steelmaking plants, focusing on areas such as the hot rolling mill, Electric Arc Furnace, and scrapyard. The approach integrates Variational Autoencoders with Gaussian Mixture Models to learn compact latent representations from Mel-spectrogram features of raw, unlabelled audio data. We compare this approach to traditional clustering techniques such as K-means and GMM, as well as Deep Embedding Clustering. The results demonstrate that the approach significantly outperforms traditional methods, offering reliable and interpretable clustering of industrial acoustic events. This research contributes to the development of automated, efficient, and sustainable noise-monitoring systems for industrial operations, addressing key challenges in environmental noise monitoring.