5 citations · 5 across the 1 of their papers we have counts for
3 papers
MLAAD: The Multi-Language Audio Anti-Spoofing Dataset
Nicolas M. Müller, Piotr Kawa, Wei Herng Choong +6
This paper presents the Multi-Language Audio Anti-Spoofing Dataset (MLAAD), version 10: a dataset of synthetic audio to train and evaluate audio deepfake detection models. It featu…
As Good as It KAN Get: High-Fidelity Audio Representation
Patryk MarszaÅek, Maciej Rut, Piotr Kawa +2
Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces…
Are audio DeepFake detection models polyglots?
BartÅomiej Marek, Piotr Kawa, Piotr Syga
Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In thi…