Speech Enhancement Based on Sparse Representation Using Joint Dictionary
Ming Wei, Zheng Liu, Xueqin Chen, Heming Zhao
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.109How to use a DOI?
- speech denoising; composite dictionary; K-SVD algorithm; speech enhancement; LARC algorithm
- A new speech denoising method that aims for processing corrupted speech signal which is based on the sparse representation theory of speech signal. In this paper, we train a composite dictionary consisting of the concatenation of the speech dictionary and the noise dictionary by using the K-SVD algorithm. Noise is divided into structured and unstructured noise in this paper. For structured noise, we train speech and noise dictionary firstly, and then according to the different coherence between speech and noise, we use LARC algorithm with a suitably chosen residual coherence threshold to realize the separation of the speech and the noise. For unstructured noise, we only need speech dictionary to extract the clean speech from corrupted speech. Experiments indicate that the proposed method gives better enhancement results in terms of quality measures of speech. The proposed method outperforms the universal dictionary speech enhancement algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ming Wei AU - Zheng Liu AU - Xueqin Chen AU - Heming Zhao PY - 2018/02 DA - 2018/02 TI - Speech Enhancement Based on Sparse Representation Using Joint Dictionary BT - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.109 DO - https://doi.org/10.2991/csece-18.2018.109 ID - Wei2018/02 ER -