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

arXiv: https://arxiv.org/abs/2202.04328 · HTML: https://mlsanity.com/paper/2202.04328/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [Multi-perspective Information Fusion Res2Net with RandomSpecmix for Fake Speech Detection](https://mlsanity.com/paper/2306.15389/) (2023)

## Abstract (arXiv)

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.