Sparse Representations for Speech Enhancement

作者：Zhao Nan^*; Xu Xin; Yang Yi

来源：Chinese Journal of Electronics, 2011, 20(2): 268-272.

• Speech enhancement
• Sparsity
• Redundant representations
• K-singular value decomposition (K-SVD)
• Orthogonal matching pursuit

This paper applies the sparse and redundant representation techniques to the problem of speech enhancement. More specifically, the K-SVD algorithm was used to train a data-driven overcomplete dictionary that describes the sparsity of speech. Orthogonal matching pursuit was employed to reconstruct the clean speech as a direct sparse decomposition technique over redundant dictionaries. Furthermore, the principle of iteration was introduced to the denoising process. When training was done on the noisy speech directly, the overall training-reconstructing algorithm became fused into one iterative procedure. Simulation shows that our proposed approach outperforms the conventional methods in terms of spectrogram analysis, objective and subjective measures.

• 出版日期2011-4
• 单位武汉大学