Open AccessRestoration of damaged speech files using deep neural networks
Author(s) -
Hee-Soo Heo,
Byung-Min So,
IL-Ho Yang,
Sung-Hyun Yoon,
Ha-Jin Yu
Publication year - 2017
Publication title -
the journal of the acoustical society of korea
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.162
H-Index - 2
eISSN - 2287-3775
pISSN - 1225-4428
DOI - 10.7776/ask.2017.36.2.136
Subject(s) - carving , computer science , speech recognition , artificial neural network , encoder , deep neural networks , artificial intelligence , operating system , engineering , mechanical engineering
In this paper, we propose a method for restoring damaged audio files using deep neural network. It is different from the conventional file carving based restoration. The purpose of our method is to infer lost information which can not be restored by existing techniques such as the file carving. We have devised methods that can automate the tasks which are essential for the restoring but are inappropriate for humans. As a result of this study it has been shown that it is possible to restore the damaged files, which the conventional file carving method could not, by using tasks such as speech or nonspeech decision and speech encoder recognizer using a deep neural network.
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