In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is proposed. The filter stochastic variations are predicted by a deep neural network (DNN) trained end-to-end using the filter residual error and signal characteristics. The presented framework allows for robust dereverberation on a single-channel noisy reverberant dataset similar to WHAMR!. The Kalman filtering WPE introduces distortions in the enhanced signal when predicting the filter variations from the residual error only, if the target speech power spectral density is not perfectly known and the observation is noisy. The proposed approach avoids these distortions by correcting the filter variations estimation in a data-driven way, increasing the robustness of the method to noisy scenarios. Furthermore, it yields a strong dereverberation and denoising performance compared to a DNN-supported recursive least squares variant of WPE, especially for highly noisy inputs.
翻译:在本文中,提出了一个神经网络增强算法,用于模拟预测错误(WPE)方法的噪音-紫外线线线脱节,配有Kalman过滤变量。过滤器随机变异由深神经网络(DNN)使用过滤器残余错误和信号特性经过培训的端对端终端预测。介绍的框架允许在类似于WHAMR的单一频道噪音变异数据集上进行强力变异!Kalman过滤 WPE在预测剩余错误的过滤变异时,只在目标语音功率光谱密度不完全为人所知且观测十分吵闹的情况下,才会在强化信号中引入扭曲。拟议方法避免这些变异,方法是以数据驱动的方式修正过滤变估计,提高方法对噪音情景的稳健性。此外,它产生强大的变异和分异性性,而DNNE支持的WPE的递回最小方形变,特别是高噪音输入。