This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (CtrSVDD). The rapid advancement of generative AI models presents significant challenges for detecting AI-generated deepfake singing voices, attracting increased research attention. The Singing Voice Deepfake Detection (SVDD) Challenge 2024 aims to address this complex task. In this work, we explore the ensemble methods, utilizing speech foundation models to develop robust singing voice anti-spoofing systems. We also introduce a novel Squeeze-and-Excitation Aggregation (SEA) method, which efficiently and effectively integrates representation features from the speech foundation models, surpassing the performance of our other individual systems. Evaluation results confirm the efficacy of our approach in detecting deepfake singing voices. The codes can be accessed at https://github.com/Anmol2059/SVDD2024.
XWSB: A Blend System Utilizing XLS-R and WavLM with SLS Classifier detection system for SVDD 2024 Challenge
College of Intelligent Systems Science and Engineering, Hubei Minzu University（湖北民族大学智能系统科学与工程学院）；School of Mathematics and Statistics, Hubei Minzu University（湖北民族大学数学与统计学院）
Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing
Department of Electrical and Computer Engineering, National University of Singapore（新加坡国立大学电子与计算机工程系）；LIGHTSPEED, Singapore（新加坡LIGHTSPEED公司）；Nanyang Technological University, Singapore（南洋理工大学）；Fortemedia Singapore, Singapore（新加坡Fortemedia公司）；Department of Electrical and Electronic Engineering and the Research Centre for Data Science & Artificial Intelligence, The Hong Kong Polytechnic University（香港理工大学电子与电气工程系及数据科学与人工智能研究中心）；Shenzhen Research Institute of Big Data, School of Artificial Intelligence, School of Data Science, The Chinese University of Hong Kong, Shenzhen, China（香港中文大学（深圳）大数据研究院、人工智能学院、数据科学学院）
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, Chinese Academy of Sciences（中国科学院人工智能学院）；School of Information and Communication Engineering, Communication University of China（中国传媒大学信息与通信工程学院）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心，清华大学）；Department of Automation, Tsinghua University（清华大学自动化系）；School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University（安徽省多模态认知计算重点实验室，安徽大学计算机科学与技术学院）；Tencent, AI Lab（腾讯，AI实验室）
