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，基于边缘计算的道路异常检测系统可同时进行数据采集。然而，边缘计算机存储容量有限，而我们需要长期存储采集到的音频样本以更新现有模型或开发新方法。因此，我们需在保证高保真音频质量的前提下，探索高效的存储管理方案。从硬件角度出发，使用低分辨率麦克风等直观方法虽可减小文件体积，但因其从根本上截断高频成分而不被推荐。相比之下，采用计算型文件压缩方法将采集的高分辨率音频编码为紧凑码字更为可取，因其还能提供对应的解码方法。受此启发，我们提出一种基于预训练自编码器的简洁高效数据压缩方法。该预训练自编码器专为音频超分辨率而训练，故可对任意采样率进行编码或解码。此外，该方法将降低从边缘端到中央服务器的数据传输通信成本。通过对比实验，我们证实该零样本音频压缩与解压缩方法在提升存储与传输效率的同时，能高度保持异常检测性能。