LISTEN: Lightweight Industrial Sound-representable Transformer for Edge Notification

TL;DR

LISTEN is a tiny, efficient industrial sound foundation model that enables real-time anomaly detection on low-cost edge devices, reducing data and hardware requirements for manufacturing applications.

Contribution

The paper introduces LISTEN, a lightweight industrial sound model using knowledge distillation, capable of real-time performance on edge devices with minimal data and training.

Findings

LISTEN achieves near-identical performance to larger models on benchmark tasks.

It operates effectively on low-cost edge hardware in real-world manufacturing settings.

Requires minimal datasets and training resources for fine-tuning.

Abstract

Deep learning-based machine listening is broadening the scope of industrial acoustic analysis for applications like anomaly detection and predictive maintenance, thereby improving manufacturing efficiency and reliability. Nevertheless, its reliance on large, task-specific annotated datasets for every new task limits widespread implementation on shop floors. While emerging sound foundation models aim to alleviate data dependency, they are too large and computationally expensive, requiring cloud infrastructure or high-end hardware that is impractical for on-site, real-time deployment. We address this gap with LISTEN (Lightweight Industrial Sound-representable Transformer for Edge Notification), a kilobyte-sized industrial sound foundation model. Using knowledge distillation, LISTEN runs in real-time on low-cost edge devices. On benchmark downstream tasks, it performs nearly identically to its much larger parent model, even when fine-tuned with minimal datasets and training resource. Beyond the model itself, we demonstrate its real-world utility by integrating LISTEN into a complete machine monitoring framework on an edge device with an Industrial Internet of Things (IIoT) sensor and system, validating its performance and generalization capabilities on a live manufacturing shop floor.