论文标题
多微粒光复合谱图用于语音覆盖
Multi-Microphone Complex Spectral Mapping for Speech Dereverberation
论文作者
论文摘要
这项研究提出了一种在固定阵列几何形状上进行语音覆盖的多微粒复合光谱映射方法。在提出的方法中，对深度神经网络（DNN）进行了训练，以预测来自多个麦克风的堆叠回响（和嘈杂）RI组件的直接声音的真实和虚构（RI）组件。我们还研究了多微晶复合物谱图与光束成形和过滤后的整合。多通道语音覆盖的实验结果证明了所提出的方法的有效性。
This study proposes a multi-microphone complex spectral mapping approach for speech dereverberation on a fixed array geometry. In the proposed approach, a deep neural network (DNN) is trained to predict the real and imaginary (RI) components of direct sound from the stacked reverberant (and noisy) RI components of multiple microphones. We also investigate the integration of multi-microphone complex spectral mapping with beamforming and post-filtering. Experimental results on multi-channel speech dereverberation demonstrate the effectiveness of the proposed approach.
