Complex-valued neural networks for voice anti-spoofing

• URL: http://arxiv.org/abs/2308.11800v1
• Date: Tue, 22 Aug 2023 21:49:38 GMT
• Title: Complex-valued neural networks for voice anti-spoofing
• Authors: Nicolas M. M\"uller, Philip Sperl, Konstantin B\"ottinger
• Abstract summary: 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. This paper proposes a new approach that combines the benefits of both methods by using complex-valued neural networks to process the input audio. 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.
• Score: 1.1510009152620668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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