{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convnext-based-neural-network-for-anti","title":"ConvNeXt Based Neural Network for Audio Anti-Spoofing","arxiv_id":"2209.06434","date":"2022-09-14","proceeding":null,"authors":["Qiaowei Ma","Jinghui Zhong","Yitao Yang","Weiheng Liu","Ying Gao","Wing W. Y. Ng"],"abstract":"With the rapid development of speech conversion and speech synthesis algorithms, automatic speaker verification (ASV) systems are vulnerable to spoofing attacks. In recent years, researchers had proposed a number of anti-spoofing methods based on hand-crafted features. However, using hand-crafted features rather than raw waveform will lose implicit information for anti-spoofing. Inspired by the promising performance of ConvNeXt in image classification tasks, we revise the ConvNeXt network architecture and propose a lightweight end-to-end anti-spoofing model. By integrating with the channel attention block and using the focal loss function, the proposed model can focus on the most informative sub-bands of speech representations and the difficult samples that are hard to classify. Experiments show that our proposed system could achieve an equal error rate of 0.64% and min-tDCF of 0.0187 for the ASVSpoof 2019 LA evaluation dataset, which outperforms the state-of-the-art systems.","url_abs":"https://arxiv.org/abs/2209.06434v5","url_pdf":"https://arxiv.org/pdf/2209.06434v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"convnext-based-neural-network-for-anti","repo_url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/convnext","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"convnext-based-neural-network-for-anti","repo_url":"https://github.com/MindSpore-paper-code-3/code2/tree/main/convnext","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"speaker-verification","task_name":"Speaker Verification"},{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"},{"task_slug":"synthetic-speech-detection","task_name":"Synthetic Speech Detection"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[{"method_slug":"convnext","method_name":"ConvNeXt"},{"method_slug":"focal-loss","method_name":"Focal Loss"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}