Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection

TL;DR

This paper introduces a zero-shot audio compression method using a pre-trained autoencoder to efficiently manage storage and transmission of road anomaly detection data at the edge, maintaining high detection accuracy.

Contribution

It presents a novel zero-shot autoencoder-based audio compression technique that preserves anomaly detection performance while reducing storage and transmission costs.

Findings

High-fidelity audio is preserved after compression and decompression.

Storage and transmission efficiency are significantly improved.

Anomaly detection performance remains robust with the proposed method.

Abstract

Recent studies show edge computing-based road anomaly detection systems which may also conduct data collection simultaneously. However, the edge computers will have small data storage but we need to store the collected audio samples for a long time in order to update existing models or develop a novel method. Therefore, we should consider an approach for efficient storage management methods while preserving high-fidelity audio. A hardware-perspective approach, such as using a low-resolution microphone, is an intuitive way to reduce file size but is not recommended because it fundamentally cuts off high-frequency components. On the other hand, a computational file compression approach that encodes collected high-resolution audio into a compact code should be recommended because it also provides a corresponding decoding method. Motivated by this, we propose a way of simple yet effective pre-trained autoencoder-based data compression method. The pre-trained autoencoder is trained for the purpose of audio super-resolution so it can be utilized to encode or decode any arbitrary sampling rate. Moreover, it will reduce the communication cost for data transmission from the edge to the central server. Via the comparative experiments, we confirm that the zero-shot audio compression and decompression highly preserve anomaly detection performance while enhancing storage and transmission efficiency.