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    题名: 高雜訊環境下基於稀疏表示法的語音強化
    其它题名: Speech enhancement based on sparse theory under noisy environment
    作者: 陳彥亨;Chen, Yan-Heng
    贡献者: 淡江大學電機工程學系碩士班
    謝景棠;Hsieh, Ching-Tang
    关键词: 語音增強;稀疏表示;Speech enhancement;sparse representations;K-SVD;Discrete consine transform(DCT);Orthogonal matching pursuit(OMP)
    日期: 2015
    上传时间: 2016-01-22 15:07:02 (UTC+8)
    摘要: 近年來,基於稀疏演算法用於訊號增強是越來越熱門的議題,此論文,我們運用稀疏演算法來增強語音訊號,我們將稀疏過程分為兩個部分:一部份為字典訓練,另一部份為語音訊號重建。乾淨語音字典是使用含雜訊語音資料利用K-SVD演算法訓練後取得,而乾淨語音字典的稀疏係數X則利用Orthogonal Matching Pursuit (OMP)演算法進行最佳化。語音訊號重建時可透過乾淨語音字典矩陣與稀疏係數矩陣相乘產生。系統則在高雜訊環境下進行評估,其環境分為白色高斯雜訊與彩色雜訊環境;同時利用四種語音客觀評估方式(SNR、LLR、SNRseg與PESQ)來評估語音去除雜訊效能。最後再與其他語音增強方法進行比較,實驗證實我們所提出的方法較優於其他語音增強方法。
    Recently, sparse algorithm for signal enhancement is more and more popular issues. In this paper, we apply it to enhance speech signal. The process of sparse theory is classified into two parts, one is for dictionary training part and the other is signal reconstruction part. We focus environment on both white Gaussian noise and color noise filtering based on sparse. The orthogonal matching pursuit (OMP) algorithm is used to optimize the sparse coefficients X of clean speech dictionary, where clean speech dictionary is trained by K-SVD algorithm. Then, we multiply these two matrixes D'' and X to reconstruct the clean speech signal. Denoising performance of the experiments shows that our proposed method is superior than other state of art methods in four kind of objective quality measures as SNR, LLR, SNRseg and PESQ.
    显示于类别:[電機工程學系暨研究所] 學位論文

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