Electrical Engineering and Systems Science > Audio and Speech Processing
arXiv:2210.06818 (eess)
[Submitted on 13 Oct 2022]
Title:Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion
Authors:Yuxiang Zhang,Jingze Lu,Xingming Wang,Zhuo Li,Runqiu Xiao,Wenchao Wang,Ming Li,Pengyuan Zhang
View a PDF of the paper titled Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion, by Yuxiang Zhang and 7 other authors
View PDFAbstract:This paper describes the deepfake audio detection system submitted to the Audio Deep Synthesis Detection (ADD) Challenge Track 3.2 and gives an analysis of score fusion. The proposed system is a score-level fusion of several light convolutional neural network (LCNN) based models. Various front-ends are used as input features, including low-frequency short-time Fourier transform and Constant Q transform. Due to the complex noise and rich synthesis algorithms, it is difficult to obtain the desired performance using the training set directly. Online data augmentation methods effectively improve the robustness of fake audio detection systems. In particular, the reasons for the poor improvement of score fusion are explored through visualization of the score distributions and comparison with score distribution on another dataset. The overfitting of the model to the training set leads to extreme values of the scores and low correlation of the score distributions, which makes score fusion difficult. Fusion with partially fake audio detection system improves system performance further. The submission on track 3.2 obtained the weighted equal error rate (WEER) of 11.04\%, which is one of the best performing systems in the challenge.
| Comments: | Accepted by ACM Multimedia 2022 Workshop: First International Workshop on Deepfake Detection for Audio Multimedia |
| Subjects: | Audio and Speech Processing (eess.AS); Sound (cs.SD) |
| Cite as: | arXiv:2210.06818 [eess.AS] |
| (orarXiv:2210.06818v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2210.06818 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1145/3552466.3556528 DOI(s) linking to related resources |
Full-text links:
Access Paper:
- View PDF
- TeX Source
- Other Formats
View a PDF of the paper titled Deepfake Detection System for the ADD Challenge Track 3.2 Based on Score Fusion, by Yuxiang Zhang and 7 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.