Research papers and academic contributions in computer vision, machine learning, and natural language processing.
This paper introduces a speaker-specific framework for detecting audio deepfakes. By combining self-supervised learning embeddings with a one-class SVM trained only on genuine speech, the method reliably identifies synthetic voices. Evaluations on benchmark and real-world datasets show strong performance across diverse spoofing techniques, making it a practical solution for safeguarding individuals, such as political figures, against audio impersonation.
This paper investigates how surface texture influences the performance of the μTesla rotor version 3. By varying the amplitude and frequency of sinusoidal textures on the rotor surfaces, the authors demonstrate that the boundary layer flow and pump output can be effectively controlled, as confirmed through simulations and experimental measurements