What do neural networks listen to? Exploring the crucial bands in Speech Enhancement using Sinc-convolution
<|reference_start|>What do neural networks listen to? Exploring the crucial bands in Speech Enhancement using Sinc-convolution: This study introduces a reformed Sinc-convolution (Sincconv) framework tailored for the encoder component of deep networks for speech enhancement (SE). The reformed Sincconv, based on parametrized sinc functions as band-pass filters, offers notable advantages in terms of training efficiency, filter diversity, and interpretability. The reformed Sinc-conv is evaluated in conjunction with various SE models, showcasing its ability to boost SE performance. Furthermore, the reformed Sincconv provides valuable insights into the specific frequency components that are prioritized in an SE scenario. This opens up a new direction of SE research and improving our knowledge of their operating dynamics.<|reference_end|>
@article{ho2024what,
    title={What do neural networks listen to? Exploring the crucial bands in Speech
  Enhancement using Sinc-convolution},
    author={Kuan-Hsun Ho, Jeih-weih Hung, and Berlin Chen},
    journal={arXiv preprint arXiv:2403.01785},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.01785},
    primaryClass={cs.SD eess.AS}
}