Audio recapture detection using deep learning
Da Luo, Haojun Wu, Jiwu Huang · 2015
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.