Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network
Joint Authors
Ye, Yongchao
Lao, Lingjie
Yan, Diqun
Wang, Rangding
Source
International Journal of Digital Multimedia Broadcasting
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Telecommunications Engineering
Electronic engineering
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Ye, Yongchao& Lao, Lingjie& Yan, Diqun& Wang, Rangding. 2020. Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network. International Journal of Digital Multimedia Broadcasting،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1169998
Modern Language Association (MLA)
Ye, Yongchao…[et al.]. Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network. International Journal of Digital Multimedia Broadcasting No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1169998
American Medical Association (AMA)
Ye, Yongchao& Lao, Lingjie& Yan, Diqun& Wang, Rangding. Identification of Weakly Pitch-Shifted Voice Based on Convolutional Neural Network. International Journal of Digital Multimedia Broadcasting. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1169998
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169998