  Current anti-spoofing and audio deepfake detection systems use either
magnitude spectrogram-based features (such as CQT or Melspectrograms) or raw
audio processed through convolution or sinc-layers. Both methods have
drawbacks: magnitude spectrograms discard phase information, which affects
audio naturalness, and raw-feature-based models cannot use traditional
explainable AI methods. This paper proposes a new approach that combines the
benefits of both methods by using complex-valued neural networks to process the
complex-valued, CQT frequency-domain representation of the input audio. This
method retains phase information and allows for explainable AI methods. Results
show that this approach outperforms previous methods on the "In-the-Wild"
anti-spoofing dataset and enables interpretation of the results through
explainable AI. Ablation studies confirm that the model has learned to use
phase information to detect voice spoofing.
