In this paper, we propose two methods to recover damaged audio files using deep neural networks. The presented audio file recovery methods differ from the conventional file carving-based recovery method because the former restore lost data, which are difficult to recover with the latter method. This research suggests that recovery tasks, which are essential yet very difficult or very time consuming, can be automated with the proposed recovery methods using deep neural networks. We apply feed-forward and Long Short Term Memory neural networks for the tasks. The experimental results show that deep neural networks can distinguish speech signals from non-speech signals, and can also identify the encoding methods of the audio files at the level of bits. This leads to successful recovery of the damaged audio files, which are otherwise difficult to recover using the conventional file-carving-based methods.

在本文中，我们提出了两种使用深度神经网络恢复损坏的音频文件的方法。提出的音频文件恢复方法与传统的基于文件雕刻的恢复方法不同，因为前者恢复丢失的数据，而后者则难以恢复。这项研究表明，恢复任务非常重要，但非常困难或非常耗时，可以使用建议的恢复方法使用深度神经网络来自动化。我们为任务应用前馈和长期短期记忆神经网络。实验结果表明，深度神经网络可以将语音信号与非语音信号区分开，并且还可以在比特级别识别音频文件的编码方法。这样可以成功恢复损坏的音频文件，