论文标题
在嘈杂的回响环境中，神经网络启动的卡尔曼过滤用于强大的在线语音覆盖
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments
论文作者
论文摘要
在本文中，提出了一种用于在线噪声的神经网络算法，并提出了加权预测误差（WPE）方法的卡尔曼过滤变体（WPE）方法。滤波器随机变化是通过使用过滤器剩余误差和信号特征端对端的深神经网络（DNN）预测的。提出的框架允许在类似于Whamr！的单渠道嘈杂的混响数据集上进行稳健的编织。当目标语音功率频谱密度不完全了解并且观察值嘈杂时，Kalman滤波WPE仅预测剩余误差的滤波器变化时，才会在增强信号中引入失真。所提出的方法通过以数据驱动的方式纠正滤波器变化估计来避免这些扭曲，从而将方法的鲁棒性增加到噪声方案。此外，与DNN支持的WPE的DNN递归最小二乘变体相比，它产生了强烈的脊椎和脱氧性能，尤其是对于高度嘈杂的输入。
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.
