In this paper, the optimal source model for the independent vector analysis (IVA) algorithm towards maximizing the output signal-to-interference ratio (SIR) is mathematically derived, and the corresponding optimal weighted covariance matrix is proved to be the covariance matrix of interference signals. A new algorithm framework called minimum variance IVA (MVIVA) is further proposed, where the deep neural network-based estimation of the interference covariance matrix is combined with the IVA-based estimation of the demixing matrix. Experimental results show the superiority of the proposed source model, and the MVIVA algorithm outperforms the original IVA algorithm by 9.6 dB in SIR and 5.8 dB in signal-to-distortion ration (SDR) on average.
翻译:本文从数学角度得出了实现输出信号对干扰比率最大化的独立的病媒分析算法的最佳来源模型,相应的最佳加权共变矩阵被证明是干扰信号的共变矩阵。还进一步提出了称为最小差异IVA(MVIVA)的新算法框架,其中以神经网络为基础对干扰共变矩阵的深度估计与基于IVA的分解矩阵估计相结合。实验结果显示拟议源模型的优越性,而MVIVA算法在信号到扭曲配给(SIR)中平均比原IVA算法高出9.6 dB和5.8 dB。