Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network

Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In...

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Bibliographic Details
Main Authors: Yongchao Ye, Lingjie Lao, Diqun Yan, Rangding Wang
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:International Journal of Digital Multimedia Broadcasting
Online Access:http://dx.doi.org/10.1155/2020/8927031
Description
Summary:Pitch shifting is a common voice editing technique in which the original pitch of a digital voice is raised or lowered. It is likely to be abused by the malicious attacker to conceal his/her true identity. Existing forensic detection methods are no longer effective for weakly pitch-shifted voice. In this paper, we proposed a convolutional neural network (CNN) to detect not only strongly pitch-shifted voice but also weakly pitch-shifted voice of which the shifting factor is less than ±4 semitones. Specifically, linear frequency cepstral coefficients (LFCC) computed from power spectrums are considered and their dynamic coefficients are extracted as the discriminative features. And the CNN model is carefully designed with particular attention to the input feature map, the activation function and the network topology. We evaluated the algorithm on voices from two datasets with three pitch shifting software. Extensive results show that the algorithm achieves high detection rates for both binary and multiple classifications.
ISSN:1687-7578
1687-7586