Automated recovery of damaged audio files using deep neural networks

Hee Soo Heo, Byung Min So, IL L.H. Yang, Sung Hyun Yoon, Ha Jin Yu

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Pages (from-to)117-126
Number of pages10
JournalDigital Investigation
StatePublished - Sep 2019


  • Audio files
  • Automated recovery
  • Deep neural networks
  • File carving
  • Long short-term memory


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