1 citations · 1 across the 5 of their papers we have counts for
5 papers
Harder or Different? Understanding Generalization of Audio Deepfake Detection
Nicolas M. Müller, Nicholas Evans, Hemlata Tak +2
Recent research has highlighted a key issue in speech deepfake detection: models trained on one set of deepfakes perform poorly on others. The question arises: is this due to the c…
Imbalance in Regression Datasets
Daniel Kowatsch, Nicolas M. Müller, Kilian Tscharke +2
For classification, the problem of class imbalance is well known and has been extensively studied. In this paper, we argue that imbalance in regression is an equally important prob…
A New Approach to Voice Authenticity
Nicolas M. Müller, Piotr Kawa, Shen Hu +4
Voice faking, driven primarily by recent advances in text-to-speech (TTS) synthesis technology, poses significant societal challenges. Currently, the prevailing assumption is that…
Protecting Publicly Available Data With Machine Learning Shortcuts
Nicolas M. Müller, Maximilian Burgert, Pascal Debus +3
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalizatio…
Complex-valued neural networks for voice anti-spoofing
Nicolas M. Müller, Philip Sperl, Konstantin Böttinger
Current anti-spoofing and audio deepfake detection systems use either magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw audio processed through convoluti…