Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks
<|reference_start|>Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks: Speech bandwidth expansion is crucial for expanding the frequency range of low-bandwidth speech signals, thereby improving audio quality, clarity and perceptibility in digital applications. Its applications span telephony, compression, text-to-speech synthesis, and speech recognition. This paper presents a novel approach using a high-fidelity generative adversarial network, unlike cascaded systems, our system is trained end-to-end on paired narrowband and wideband speech signals. Our method integrates various bandwidth upsampling ratios into a single unified model specifically designed for speech bandwidth expansion applications. Our approach exhibits robust performance across various bandwidth expansion factors, including those not encountered during training, demonstrating zero-shot capability. To the best of our knowledge, this is the first work to showcase this capability. The experimental results demonstrate that our method outperforms previous end-to-end approaches, as well as interpolation and traditional techniques, showcasing its effectiveness in practical speech enhancement applications.<|reference_end|>
@article{salhab2024speech,
    title={Speech Bandwidth Expansion Via High Fidelity Generative Adversarial
  Networks},
    author={Mahmoud Salhab and Haidar Harmanani},
    journal={arXiv preprint arXiv:2407.18571},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.18571},
    primaryClass={cs.SD cs.AI eess.AS}
}