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.

翻译：近期研究表明，基于边缘计算的声学路面异常检测系统可同步进行数据采集。然而，边缘计算机存储空间有限，但需长期保存采集的音频样本以更新现有模型或开发新方法。因此，需研究既能保持高保真音频又能实现高效存储的管理策略。从硬件层面着手，如采用低分辨率麦克风这类直观方法虽能降低文件体积，但因从根本上截断高频成分而不可取。反之，采用将采集的高分辨率音频编码为紧凑码流的计算型压缩方法更为合理，因其同时提供对应的解码方案。受此启发，我们提出一种基于预训练自编码器的简单高效数据压缩方法。该预训练自编码器专为音频超分辨率任务而训练，可实现对任意采样率音频的编解码操作。该方法还能降低边缘端至中心服务器间数据传输的通信成本。对比实验证明，该零样本音频压缩与解压缩方法能在提升存储与传输效率的同时，最大程度保持异常检测性能。