Speech enhancement via sparse coding with ideal binary mask

An improved algorithm is presented for speech enhancement via sparse representation and ideal binary mask (IBM) methods. In the traditional IBM, the basic idea is to identify voiced components as target signal and label unvoiced ones as interference noise vice versa. However, such voiced and unvoiced components still cannot be well separated in target signal and interference noise. To fully exploit the merits of sparse representation theory, we extract the exact voiced component from both the above twofold to obtain the final enhanced speech. Experimental results demonstrate the proposed method can achieve higher PESQ scores than the traditional IBM to efficiently improve speech intelligibility.