基于小波變換的自適應(yīng)多分辨率語音增強(qiáng)算法
AN ADAPTIVE MULTIRESOLUTION SPEECH ENHANCEMENT ALGORITHM BASED ON WAVELET TRANSFORM
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摘要: 本文提出了一種基于小波變換的自適應(yīng)多分辨率語音增強(qiáng)算法,它在尺度上和尺度間同時對受噪聲污染的語音信號作自適應(yīng)濾波處理,從而使得對聽覺影響最嚴(yán)重的頻段上的噪聲被有效地濾除掉,濾波后的語音信噪比和主觀聽覺質(zhì)量都得到了很大的改善。
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關(guān)鍵詞:
- 小波變換; 多分辨分析; 自適應(yīng)濾波; 語音增強(qiáng)
Abstract: 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. -
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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