Speech Enhancement based on Sparse Theory under Noisy Environment
| Autor: | Yan-Heng Chen, 陳彥亨 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 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. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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