LCAA: Lightweight Convolutional Attention Autoencoder for Acoustic Anomaly Detection

Industrial machinery monitoring is pivotal in modern manufacturing, where unexpected equipment failures could incur significant economic and operational costs. In this work, we introduce LCAA,a novel unsupervised framework tailored for acoustic anomaly detection in industrial environments. Our approach synergistically combines convolutional neural networks with multi-head attention mechanisms within a compact autoencoder architecture, enabling the effective capture of both temporal and frequency domain features inherent in acoustic signals. By selectively focusing on the most informative components of the input, the proposed model enhances feature extraction, leading to improved detection accuracy and faster convergence compared to traditional methods. Extensive experiments on multiple benchmark datasets demonstrate that LCAA not only outperforms state-of-the-art baselines in detecting subtle anomalies but also maintains a minimal parameter footprint, thereby facilitating real-time deployment on resource-constrained edge devices. This study contributes a robust and efficient solution for proactive maintenance strategies, promoting enhanced operational reliability and reduced downtime in industrial systems.