CAJ | 학술논문

主要研究了欠定盲源分离中的混合矩阵估计问题。提出了一种检测时频单源点的新方法，通过比较归一化的观测信号时频点的实部和虚部向量来检测时频单源点。与其他时频单源点检测方法相比，该方法简单而有效，同时降低了对检测条件的要求。采用K-means方法估计混合矩阵，通过去除聚类后每一类数据中偏离中心方向较远的数据点，进一步提高了混合矩阵的估计精度。仿真实验表明，与已有欠定混合矩阵估计算法相比，提出的方法有更高的估计精度。
주요연구료흠정맹원분리중적혼합구진고계문제。제출료일충검측시빈단원점적신방법，통과비교귀일화적관측신호시빈점적실부화허부향량래검측시빈단원점。여기타시빈단원점검측방법상비，해방법간단이유효，동시강저료대검측조건적요구。채용K-means방법고계혼합구진，통과거제취류후매일류수거중편리중심방향교원적수거점，진일보제고료혼합구진적고계정도。방진실험표명，여이유흠정혼합구진고계산법상비，제출적방법유경고적고계정도。
This paper focuses on the mixing matrix estimation in Underdetermined Blind Source Separation(UBSS). A method of single source points detection in the Time-Frequency(TF)domain is proposed, which detects the points by comparing the normalized real and imaginary parts of the mixtures. The proposed method is simple and effective, which also relaxes the condition on detecting the single source points compared with other existing method. Then K-means clustering method is adopted to estimate the mixing matrix. The performance of the mixing matrix estimation is further improved by removing those points which are far away from the mean direction of each cluster. It is experimentally shown that the proposed mixing matrix estimation algorithm estimates the mixing matrix with high accuracy compared with other algorithms.