RawBMamba: End-to-End Bidirectional State Space Model for Audio Deepfake Detection

Understand

Fake artefacts for discriminating between bonafide and fake audio can exist in both short- and long-range segments.

• Therefore, combining local and global feature information can effectively discriminate between bonafide and fake audio.
• This paper proposes an end-to-end bidirectional state space model, named RawBMamba, to capture both short- and long-range discriminative information for audio deepfake detection.
• Specifically, we use sinc Layer and multiple convolutional layers to capture short-range features, and then design a bidirectional Mamba to address Mamba's unidirectional modelling problem and further capture long-range feature information.