论文标题
可自定义的端到端优化在线神经网络支持的听力设备
Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices
论文作者
论文摘要
这项工作着重于使用加权预测误差（WPE）算法的在线缩放用于听力设备。 WPE过滤需要估计目标语音功率频谱密度（PSD）。最近，深层神经网络（DNN）已用于此任务。但是，这些方法优化了只有间接影响WPE输出的PSD估计值，从而可能导致有限的去脊椎。在本文中，我们提出了一种专门用于在线处理的端到端方法，该方法直接优化了REREVERBER的输出信号。此外，我们建议通过修改优化目标以及培训中使用的WPE算法特征来使其适应不同类型的听力设备用户的需求。我们表明，所提出的端到端方法在无噪声版本的WHAMR版本上优于传统和传统的DNN支持的WPE！数据集。
This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm. WPE filtering requires an estimate of the target speech power spectral density (PSD). Recently deep neural networks (DNNs) have been used for this task. However, these approaches optimize the PSD estimate which only indirectly affects the WPE output, thus potentially resulting in limited dereverberation. In this paper, we propose an end-to-end approach specialized for online processing, that directly optimizes the dereverberated output signal. In addition, we propose to adapt it to the needs of different types of hearing-device users by modifying the optimization target as well as the WPE algorithm characteristics used in training. We show that the proposed end-to-end approach outperforms the traditional and conventional DNN-supported WPEs on a noise-free version of the WHAMR! dataset.
