Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution

Page view(s)
27
Checked on Aug 30, 2024
Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution
Title:
Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution
Journal Title:
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
13 May 2021
Citation:
Liu, M., Wang, L., Lee, K. A., Chen, X., Dang, J. (2021). Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp39728.2021.9414250
Abstract:
Variable-resolution processing aims to improve the feature representation ability by enlarging the local discriminative details. In previous anti-spoofing studies, different phones and frequency regions were both proven to have various levels of sensitivity to replay distortion. In this paper, an adaptive spectro-temporal resolution is proposed to obtain the optimal scale in the feature space: the frequency resolution is adaptive to frequency discrimination, while the temporal resolution is adaptive to continuous phones. In the process, phone-frequency F-ratio analysis is applied to investigate the sensitivity divergences to replay distortion among phones and frequencies. Then, attentive filters are designed to automatically adapt to the phone-frequency discrimination. Validation experiments for the proposed method are conducted on two well-acknowledged magnitude and phase features. A comparative analysis on the ASVspoof 2017 V2.0 database demonstrates that our proposed adaptive spectro-temporal resolution method attains considerably higher error reduction rates than the approaches involving the corresponding original resolution features.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the The National Key R&D Program of China - -
Grant Reference no. : 2018YFB1305200

This research / project is supported by the National Natural Science Foundation of China - -
Grant Reference no. : 61771333

This research / project is supported by the Tianjin Municipal Science and Technology Project - -
Grant Reference no. : 18ZXZNGX00330
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2379-190X
1520-6149
ISBN:
978-1-7281-7605-5
978-1-7281-7606-2
Files uploaded:

File Size Format Action
icassp2021-v6.pdf 413.66 KB PDF Open