基于时频分布的欠定混叠盲分离

Underdetermined Blind Source Separation(BSS):A Time-Frequency Approach

针对欠定混合信号的盲分离问题,提出了基于时频分布的欠定盲分离算法,首先计算信号的时频分布矩阵并找出信号的自源时频点,然后把自源点对应的时频分布矩阵表示成三阶张量并通过张量分解估计出混合矩阵,最后通过计算矩阵的伪逆和时频合成来完成源信号的恢复.该算法不需要假设源信号是稀疏的或相互独立的.仿真结果表明与已有算法相比本文方法提高了盲分离的性能.

Underdetermined blind source separation（BSS） is discussed.Based on time-frequency analysis,an underdetermined BSS method is developed,in which sources are not necessarily sparse or independent mutually.First,we compute a sequence of matrices of time-frequency distributions（TFDs） and obtain the auto-source TF points,then we fold the TFD matrices into a third-order tensor and calculate the mixing matrix by tensor canonical decomposition,finally we obtain the sources by calculating the pseudo-inverse matrix and TF synthesis techniques.The simulations demonstrate that the proposed method outperforms the existing methods in performance significantly.