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JBE, vol. 23, no. 1, pp.86-92, January, 2018


Coding History Detection of Speech Signal using Deep Neural Network

Hyo-Jin Cho, Won Jang, Seong-Hyeon Shin, and Hochong Park

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In this paper, we propose a method for coding history detection of digital speech signal. In digital speech communication and storage, the signal is encoded to reduce the number of bits. Therefore, when a speech signal waveform is given, we need to detect its coding history so that we can determine whether the signal is an original or an coded one, and if coded, determine the number of times of coding. In this paper, we propose a coding history detection method for 12.2kbps AMR codec in terms of original, single coding, and double coding. The proposed method extracts a speech-specific feature vector from the given speech, and models the feature vector using a deep neural network. We confirm that the proposed feature vector provides better performance in coding history detection than the feature vector computed from the general spectrogram.


Keyword: coding history, feature vector, speech parameter, DNN

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