基于稀疏性的欠定语音盲分离方法研究

针对源信号增多导致语音信号稀疏性变差的问题，提出一种新的基于稀疏性的混合矩阵估计方法，利用主分量分析(PCA)检测只有一个源信号存在的时频点并用于估计混合矩阵，从而提高了估计性能，特别适用于欠定语音盲分离。同时指出了影响基于稀疏性语音盲分离方法性能的因素。仿真结果验证了上述结论。

Underdetermined blind speech separation of sparseness

A new sparseness-based method was proposed for mixing matrix estimation, in the case of poor sparseness of speech signals with increasing number of sources. The time-frequency bins with only one source were detected by Principal Component Analysis (PCA), and then were exploited to estimate the mixing matrix to improve the estimation performance. The proposed method is especially applicable to underdetermined blind speech separation. The reasons deteriorating the performance of blind speech separation were also pointed out. The simulation results demonstrate the conclusions above.