{"title":"What do neural networks listen to? Exploring the crucial bands in Speech Enhancement using Sinc-convolution","authors":"Kuan-Hsun Ho, J. Hung, Berlin Chen","doi":"10.1109/icassp48485.2024.10445878","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":513202,"journal":{"name":"ArXiv","volume":"101 10‐12","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ArXiv","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/icassp48485.2024.10445878","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
本研究介绍了一种为语音增强（SE）深度网络编码器组件量身定制的改革 Sinc-卷积（Sincconv）框架。改革后的 Sincconv 基于参数化 sinc 函数作为带通滤波器，在训练效率、滤波器多样性和可解释性方面具有显著优势。改革后的 Sincconv 结合各种 SE 模型进行了评估，展示了其提高 SE 性能的能力。此外，改革后的 Sincconv 对 SE 场景中优先考虑的特定频率成分提供了有价值的见解。这开辟了 SE 研究的新方向，并提高了我们对其运行动态的认识。
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