Abstract:
Since the audio recapture can be used to assist audio splicing, it is important to identify whether a suspected audio recording is recaptured or not. However, few works o...Show MoreMetadata
Abstract:
Since the audio recapture can be used to assist audio splicing, it is important to identify whether a suspected audio recording is recaptured or not. However, few works on such detection have been reported. In this paper, we propose an method to detect the recaptured audio based on deep learning and we investigate two deep learning techniques, i.e., neural network with dropout method and stack auto-encoders (SAE). The waveform samples of audio frame is directly used as the input for the deep neural network. The experimental results show that error rate around 7.5% can be achieved, which indicates that our proposed method can successfully discriminate recaptured audio and original audio.
Published in: 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP)
Date of Conference: 12-15 July 2015
Date Added to IEEE Xplore: 03 September 2015
ISBN Information: