论文标题
Cau_ku团队提交的添加2022挑战任务1：通过频率功能屏蔽效率低质量的假音频检测
CAU_KU team's submission to ADD 2022 Challenge task 1: Low-quality fake audio detection through frequency feature masking
论文作者
论文摘要
该技术报告描述了Chung-Ang University和Korea University（CAU_KU）团队的模型，该模型参与了Audio Deep Synthesis检测（ADD）2022 Challenge，曲目1：低质量的假音频检测。对于轨道1，我们提出了一种频率功能屏蔽（FFM）增强技术来处理低质量的音频环境。可以应用基于频谱图的模型的检测％。我们在五个基于频谱图的深度神经网络体系结构上应用了FFM和Mixup Evermantion，这些结构可用于使用MEL-SPECTROGRAM和CONSTER Q变换（CQT）功能进行欺骗检测。我们最好的提交在轨道1上获得了第三位EER的23.8％。
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.
