Abstract: A blind speech source separation method for the underdetermined convolution is proposed via combining Fast Independent Component Analysis（FastICA） and adaptive nonlinear binary time-frequency masking.By estimating nonlinear binary masks from the outputs of a FastICA algorithm，it is possible in an iterative way to extract basic speech signals from a convolutive mixture.The basic signals are afterwards improved by the masks merging.The stereo property of the extracted speech signals can be maintained.The simulation results demonstrate that the proposed separation method outperforms DUET and BLUES methods.The signal-noise-ratio gain of the results is greatly improved.

摘要： 针对语音信号的欠定卷积混合模型，提出一种基于快速独立分量分析和自适应非线性二元时频掩蔽的语音盲分离方法。对输入的混合语音信号进行快速独立分量分析，将结果进行自适应非线性二元时频掩蔽；重复进行这两步处理，直到分离出所有的语音源信号。将分离出的语音源信号，再通过二元时频掩蔽合并可提高输出的质量，分离出的语音信号仍然能保留双声道立体声的效果。实验表明，该方法的性能大大优于DUET方法和BLUES方法，信噪比增益大幅提高。