An improved dictionary learning method for speech enhancement

In this paper, an improved dictionary learning method for speech enhancement is proposed. Given prior information of the noise, the dictionaries of speech and noise are firstly trained by an approximate KSVD algorithm, respectively. Then, the estimated short-time Fourier transform (STFT) magnitudes of speech and noise can be sparsely represented by multiplying the dictionary with sparse coefficients, which are calculated by the least angle regression (LAR) algorithm. A geometrical stopping criterion with an adaptive threshold is utilized to adjust the conventional stopping criterion in LAR algorithm so that it can increase the adaptability of LAR. Next, we propose a framework that utilizes the expectation maximization (EM) method to refine the energy of the estimated speech and noise in order to obtain more accurate estimation of STFT magnitudes. Finally, a modified wiener filter is constructed to further eliminate residual noise. When the prior information of noise is unknown, an online noise estimation method is applied to replace the noise dictionary. The test results show that the proposed method outperforms the reference speech enhancement methods.