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Volume 20 Issue 3
May  1998
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Zheng Yuanjin, Li Lemin, Wen Maosheng. AN ADAPTIVE MULTIRESOLUTION SPEECH ENHANCEMENT ALGORITHM BASED ON WAVELET TRANSFORM[J]. Journal of Electronics & Information Technology, 1998, 20(3): 289-295.
Citation: Zheng Yuanjin, Li Lemin, Wen Maosheng. AN ADAPTIVE MULTIRESOLUTION SPEECH ENHANCEMENT ALGORITHM BASED ON WAVELET TRANSFORM[J]. Journal of Electronics & Information Technology, 1998, 20(3): 289-295.

AN ADAPTIVE MULTIRESOLUTION SPEECH ENHANCEMENT ALGORITHM BASED ON WAVELET TRANSFORM

  • Received Date: 1996-07-08
  • Rev Recd Date: 1997-08-11
  • Publish Date: 1998-05-19
  • In this paper, an adaptive multiresolution speech enhancement algorithm based on wavelet transform is put forward. It can make adaptive filtering to noise speech both at scales and among scales. So that the noise parts during the frequency intervals which decrease hearing quality mostly are reduced efficiently. Both the SNR and subject hearing quality of denoised speech are high and good.
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  • Delopoulous A, Kollias S. Optimal filter banks for signal reconstruction from noisy subband components. IEEE Trans. on SP, 1996, SP-44(2):212-224.[2]Mallat S. A theory for multiresolution signal decomposition: The wavlet representation. IEEE Trans. on PAMI, 1989, PAMI-11(7): 674-691.[3]Mallat S, Liang Hwang Wen. Singularity detection and processing with wavelet. IEEE Trans. on IT, 1992, IT-38(2): 617-643.[4]Qinghua Zhang, Benveniste Albert. Wavlet networks. IEEE Trans. on NN, 1992, NN-3(6): 889-898.[5]Sahiner B, Yagle A E. Image reconstruction from projection under wavelet constraints. IEEE Trans. on SP,1993, SP-41(12): 3579-3583.[6]Gilloire A, Vetterli M. Adaptive filtering in subband. in Proc. IEEE Int. Conf. on ASSP, New York: Apr.,1988, 1572-1575.[7]Zhang J, Walter G G, Lee W N W. Wavelet neural networks for function learning. IEEE Trans. on SP, 1995, SP-43(6): 1489-1496.
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