论文标题
深层神经网络源先验的独立矢量分析
Independent Vector Analysis with Deep Neural Network Source Priors
论文作者
论文摘要
本文研究了独立媒介分析（IVA）的密度先验，并将其备量言语混合物分离为示例性应用。 IVA的大多数现有源先验都过于简化，无法捕获演讲的精细结构。在这里，我们第一次表明，可以通过优化某些相关的绩效指数（例如深层神经网络（DNN））等通用近似值（例如深神经网络（DNN））有效地估计语音密度的导数。实验结果表明，用于在线实施和信噪比（SIR）以实施批处理的神经网络密度先验在收敛速度方面的表现始终优于以前的神经网络密度。
This paper studies the density priors for independent vector analysis (IVA) with convolutive speech mixture separation as the exemplary application. Most existing source priors for IVA are too simplified to capture the fine structures of speeches. Here, we first time show that it is possible to efficiently estimate the derivative of speech density with universal approximators like deep neural networks (DNN) by optimizing certain proxy separation related performance indices. Experimental results suggest that the resultant neural network density priors consistently outperform previous ones in convergence speed for online implementation and signal-to-interference ratio (SIR) for batch implementation.
