Anomalous Sound Detection

Anomalous sound detection (ASD) focuses on identifying unusual sounds in audio data, typically to detect machine malfunctions or other anomalies in industrial settings. Current research emphasizes unsupervised and semi-supervised learning approaches, leveraging autoencoders, diffusion models, and transformers, often incorporating techniques like contrastive learning and self-supervised training to address data scarcity and domain shifts. These advancements are crucial for improving the reliability and efficiency of industrial monitoring systems, enabling early fault detection and preventative maintenance, and reducing downtime.