CAU_KU team's submission to ADD 2022 Challenge task 1: Low-quality fake audio detection through frequency feature masking

Autor:	Kwak, Il-Youp, Choi, Sunmook, Yang, Jonghoon, Lee, Yerin, Oh, Seungsang
Rok vydání:	2022
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	This technical report describes Chung-Ang University and Korea University (CAU_KU) team's model participating in the Audio Deep Synthesis Detection (ADD) 2022 Challenge, track 1: Low-quality fake audio detection. For track 1, we propose a frequency feature masking (FFM) augmentation technique to deal with a low-quality audio environment. %detection that spectrogram-based models can be applied. We applied FFM and mixup augmentation on five spectrogram-based deep neural network architectures that performed well for spoofing detection using mel-spectrogram and constant Q transform (CQT) features. Our best submission achieved 23.8% of EER ranked 3rd on track 1.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.04328 Zobrazit plný text záznamu View this record from Arxiv