This letter describes a time-varying extension of independent vector analysis (IVA) based on the normalizing flow (NF), called NF-IVA, for determined blind source separation of multichannel audio signals. As in IVA, NF-IVA estimates demixing matrices that transform mixture spectra to source spectra in the complex-valued spatial domain such that the likelihood of those matrices for the mixture spectra is maximized under some non-Gaussian source model. While IVA performs a time-invariant bijective linear transformation, NF-IVA performs a series of time-varying bijective linear transformations (flow blocks) adaptively predicted by neural networks. To regularize such transformations, we introduce a soft volume-preserving (VP) constraint. Given mixture spectra, the parameters of NF-IVA are optimized by gradient descent with backpropagation in an unsupervised manner. Experimental results show that NF-IVA successfully performs speech separation in reverberant environments with different numbers of speakers and microphones and that NF-IVA with the VP constraint outperforms NF-IVA without it, standard IVA with iterative projection, and improved IVA with gradient descent.

中文翻译：

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盲源分离的基于流的独立矢量分析

这封信描述了基于归一化流 (NF) 的独立矢量分析 (IVA) 的时变扩展，称为 NF-IVA，用于确定多声道音频信号的盲源分离。与在 IVA 中一样，NF-IVA 估计将混合光谱转换为复值空间域中的源光谱的去混合矩阵，以便在某些非高斯源模型下最大化混合光谱的这些矩阵的可能性。IVA 执行时不变双射线性变换，而 NF-IVA 执行一系列由神经网络自适应预测的时变双射线性变换（流块）。为了规范这种转换，我们引入了软体积保留 (VP) 约束。给定混合光谱，NF-IVA 的参数通过梯度下降和反向传播以无监督的方式进行优化。实验结果表明，NF-IVA 在具有不同扬声器和麦克风数量的混响环境中成功执行语音分离，并且具有 VP 约束的 NF-IVA 优于没有它的 NF-IVA、具有迭代投影的标准 IVA 和具有梯度下降的改进 IVA。