Enhancing Anomalous Sound Detection with Multi-Level Memory Bank

Abnormal sound detection (ASD) is crucial for the timely detection of machine faults in industrial scenarios and has emerged as a popular topic. However, exhaustively collecting all ever-changing anomalous samples is impractical for the associated time and cost. Under unsupervised conditions, identifying rare or even unseen abnormal sounds from a large set of normal samples is a notable challenge in the real-world setting. To address this, we propose a novel ASD method based on a multi-level memory bank to estimate the distribution of normal samples in the latent space. We employ a distance-based metric to distinguish inliers from outliers, leveraging high, mid, and low-level features to improve accuracy. We also propose an acoustic-aware farthest embedding sampling algorithm for inference acceleration and memory bank reduction. Experimental results demonstrate our method outperforms existing methods for anomaly detection. Additionally, we analyze the effect of multilevel and acoustic-aware farthest embedding sampling methods, respectively.