In this paper, we propose a new sparse decomposition based single-channel speech separation method using orthogonal matching pursuit (OMP). The separation is performed using source-individual dictionaries consisting of time-domain training frames as atoms. OMP is used to compute sparse coefficients to estimate sources. We report the separation results of our proposed method and compare them with a separation method based on sparse non-negative matrix factorization (SNMF) which is a classical sparse decomposition based separation method. Experiments show that our proposed method results in higher signal-to-noise ratio (SNR) and signal-to-interference ratio (SIR).
['Haiyan Guo', 'Xiaoxiong Li', 'Lin Zhou', 'Zhenyang Wu']
Single-channel Speech Separation Using Orthogonal Matching Pursuit