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  • 标题:New Speech Enhancement based on Discrete Orthonormal Stockwell Transform
  • 本地全文:下载
  • 作者:Safa SAOUD ; Souha BOUSSELMI ; Mohamed BEN NASER
  • 期刊名称:International Journal of Advanced Computer Science and Applications(IJACSA)
  • 印刷版ISSN:2158-107X
  • 电子版ISSN:2156-5570
  • 出版年度:2016
  • 卷号:7
  • 期号:10
  • DOI:10.14569/IJACSA.2016.071026
  • 出版社:Science and Information Society (SAI)
  • 摘要:S-transform is an effective time-frequency representation which gives simultaneous frequency and time distribution information alike the wavelet transforms (WT). However, the ST redundantly doubles the dimension of the original data set and the Discrete Orthonormal S-Transform (DOST) can decrease the redundancy of S-transform farther. So, this paper aims to propose a new method to remove additive background noise from noisy speech signal using DOST which supplies a multi-resolution analysis (MRA) spatial-frequency representation of image processing and signal analysis. Hence, the performances of the applied speech enhancement technique have been evaluated objectively and subjectively in comparison with respect to many other methods in four background noises at different SNR levels.
  • 关键词:thesai; IJACSA Volume 7 Issue 10; MRA; Stockwell Transform; DOST; DWT; speech enhancement
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