摘要
针对语音信号的弱时频正交性,提出一种基于主分量分析的混合矩阵估计方法。在时频域中,允许每个时频点存在任意多个源信号,通过对每个时频点进行主分量分析,检测只有一个源信号存在的时频点,此类时频点最大特征值对应的特征向量即为混合向量的一个估计,因此对所有估计出的混合向量进行K均值聚类,将聚类中心作为混合矩阵的估计。实验仿真表明,提出的方法提高了混合矩阵的估计精度,特别适用于估计欠定情况下的混合矩阵。
In blind speech separation, a method based on principal component analysis （PCA） is proposed to estimate the mixing matrix for the weak time-frequency orthogonality property of speech. In the time-frequency domain, the proposed method allows the arbitrary number of sources to be existed in a time-frequency bin, then PCA is applied to every time-frequency bin to detect the existed one source in the time-frequency bins. In the detected time-frequency bins, the eigenvector associated with the maximum eigenvalue is an estimation of the mixing vectors, so K-means clustering is exploited on all the mixing vectors and the cluster centers are used as the estimation of the mixing matrix. Simulation results demonstrate that the proposed method can improve estimation precision, especially for estimating the mixing matrix in under- determined case.
出处
《数据采集与处理》
CSCD
北大核心
2010年第1期18-22,共5页
Journal of Data Acquisition and Processing
基金
国家自然科学基金(60672157
60672158)资助项目
