| pipeline_tag: audio-classification | |
| # WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection | |
| This repository contains the **WaveSP-Net** model, a novel architecture for speech deepfake detection, presented in the paper [WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection](https://huggingface.co/papers/2510.05305). | |
| WaveSP-Net introduces parameter-efficient front-ends that fuse prompt-tuning with classical signal processing transforms and combines a Partial-WSPT-XLSR front-end with a bidirectional Mamba-based back-end. This design effectively enhances the localization of subtle synthetic artifacts without altering frozen XLSR parameters, leading to superior performance on challenging benchmarks with low trainable parameters. | |
| For the official code and more details, please refer to the [GitHub repository](https://github.com/xxuan-acoustics/WaveSP-Net). | |
| ## Citation | |
| If you find **WaveSP-Net** or the associated code helpful for your research, please kindly cite our paper: | |
| ```bibtex | |
| @misc{xuan2025wavespnet, | |
| title={WaveSP-Net: Learnable Wavelet-Domain Sparse Prompt Tuning for Speech Deepfake Detection}, | |
| author={Xi Xuan and Xuechen Liu and Wenxin Zhang and Yi-Cheng Lin and Xiaojian Lin and Tomi Kinnunen}, | |
| year={2025}, | |
| eprint={2510.05305}, | |
| archivePrefix={arXiv}, | |
| primaryClass={eess.AS}, | |
| url={https://arxiv.org/abs/2510.05305}, | |
| } | |
| ``` |