High detection rates of Torque teno sus virus in co-infection with important viral pathogens in porcine kidneys on St. Kitts Island, Lesser Antilles

2018 ◽  
Vol 65 (5) ◽  
pp. 1175-1181 ◽  
Author(s):  
Souvik Ghosh ◽  
Esteban Soto ◽  
Oscar Illanes ◽  
Ryan Navarro ◽  
Meiji Soe Aung ◽  
...  
2018 ◽  
Vol 65 ◽  
pp. 131-135 ◽  
Author(s):  
Souvik Ghosh ◽  
Kanae Shiokawa ◽  
Meiji Soe Aung ◽  
Yashpal S. Malik ◽  
Nobumichi Kobayashi

2009 ◽  
Vol 136 (2) ◽  
pp. 459-470 ◽  
Author(s):  
Hongzhi Zou ◽  
William R. Taylor ◽  
Jonathan J. Harrington ◽  
Fareeda Taher Nazer Hussain ◽  
Xiaoming Cao ◽  
...  

2020 ◽  
Vol 84 ◽  
pp. 104383
Author(s):  
Soh Jiaying Joycelyn ◽  
Agnes Ng ◽  
Alyssa Kleymann ◽  
Yashpal S. Malik ◽  
Nobumichi Kobayashi ◽  
...  

Author(s):  
Shounak Majumder ◽  
William R. Taylor ◽  
Patrick H. Foote ◽  
Calise K. Berger ◽  
Chung Wah Wu ◽  
...  

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yongchao Ye ◽  
Lingjie Lao ◽  
Diqun Yan ◽  
Rangding Wang

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.


2017 ◽  
Vol 56 (8) ◽  
pp. 895-902 ◽  
Author(s):  
Toshinobu Yokoyama ◽  
Takashi Kinoshita ◽  
Masaki Okamoto ◽  
Kazuko Matsunaga ◽  
Tomoko Kamimura ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document