Single-channel separation using underdetermined blind autoregressive model and least absolute deviation

摘要

A novel "artificial stereo" mixture is proposed to resemble a synthetic stereo signal for solving the signal-channel blind source separation (SCBSS) problem. The proposed SCBSS framework takes the advantages of the following desirable properties: one microphone; no training phase; no parameter turning; independent of initialization and a priori data of the sources. The artificial stereo mixture is formulated by weighting and time-shifting the single-channel observed mixture. Separability analysis of the proposed mixture model has also been elicited to examine that the artificial stereo mixture is separable. For the separation process, mixing coefficients of sources are estimated where the source signals are modeled by the autoregressive process. Subsequently, a binary time-frequency mask can then be constructed by evaluating the least absolute deviation cost function. Finally, experimental testing on autoregressive sources has shown that the proposed framework yields superior separation performance and is computationally very fast compared with existing SCBSS methods.