We study a time domain decorrelation method of source signal separation from convolutive sound mixtures based on an infinite impulse response （IIR） model. The IIR model uses fewer parameters to capture the physical ...We study a time domain decorrelation method of source signal separation from convolutive sound mixtures based on an infinite impulse response （IIR） model. The IIR model uses fewer parameters to capture the physical mixing process and is useful for finding low dimensional separating solutions. We present inversion formulas to decorrelate the mixture signals and derive filter equations involving second order time lagged statistics of mixtures. We then formulate an 11 constrained minimization problem and solve it by an iterative method. Numerical experiments on recorded sound mixtures show that our method is capable of sound separation in low dimensional parameter spaces with good perceptual quality and low correlation coefficient comparable to the known infomax method.展开更多
基金partially supported by NSF grants DMS-0712881, NIH grant 2R44DC006734the CORCLR (Academic Senate Council on Research, Computing and Library Resources) faculty research grant MI-2006-07-6, and a Pilot award of the Center for Hearing Research at UC Irvine
文摘We study a time domain decorrelation method of source signal separation from convolutive sound mixtures based on an infinite impulse response （IIR） model. The IIR model uses fewer parameters to capture the physical mixing process and is useful for finding low dimensional separating solutions. We present inversion formulas to decorrelate the mixture signals and derive filter equations involving second order time lagged statistics of mixtures. We then formulate an 11 constrained minimization problem and solve it by an iterative method. Numerical experiments on recorded sound mixtures show that our method is capable of sound separation in low dimensional parameter spaces with good perceptual quality and low correlation coefficient comparable to the known infomax method.
