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    題名: SPEECH ENHANCEMENT BASED ON SPARSE THEORY UNDER NOISY ENVIRONMENT
    作者: Hsieh, Ching-Tang;Chen, Yan-heng;Chen, Ting-Wen;Chen, Li-Ming
    貢獻者: 電機工程學系暨研究所
    關鍵詞: Speech enhancement, sparse representations, K-SVD, discrete cosine transform (DCT), orthogonal matching pursuit (OMP)
    日期: 2015-07-18
    上傳時間: 2015-07-27 14:05:46 (UTC+8)
    出版者: Academy of Taiwan Information Systems Research (ATISR)
    摘要: Recently, the sparse algorithm for sparse enhancement is more and more popular issues. In this paper, we classify the process of the sparse theory to enhance speech signal into two parts, one is for dictionary training part and the other is signal reconstruction part. We focus on the White Gaussian Noise. Clean speech dictionary D is trained by K-SVD algorithm. The orthogonal matching pursuit(OMP) algorithm is used to obtain the sparse coefficients X of clean speech dictionary D. Denoising performance of the experiments shows that our proposed method is superior than other methods in SNR, LLR, SNRseg and PESQ.
    關聯: International Conference on Internet Studies (NETs 2015)
    顯示於類別:[電機工程學系暨研究所] 會議論文

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