¹School of Information Science and Engineering, Southeast University, Nanjing, China; College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu, China
²School of Information Science and Engineering, Southeast University, Nanjing, China

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Abstract—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).

Index Terms—Single-channel speech separation (SCSS), sparse decomposition, orthogonal matching pursuit (OMP), dictionary.

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Cite: Haiyan Guo, Xiaoxiong Li, Lin Zhou, Zhenyang Wu, "Single-channel Speech Separation Using Orthogonal Matching Pursuit," Journal of Software vol. 9, no. 11, pp. 2974-2980, 2014.