Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection
YeongHyeon Park, Uju Gim, Myung Jin Kim

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…
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Taxonomy
TopicsMusic and Audio Processing · Anomaly Detection Techniques and Applications · Speech and Audio Processing
