论文标题
综合波 - 几何冲动响应，以改善语音覆盖
Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation
论文作者
论文摘要
我们提出了一种新颖的方法，可以使用准确的合成数据集提高基于学习的语音覆盖的性能。我们的方法旨在从混响的语音信号中恢复无混响信号。我们表明，准确模拟房间脉冲响应（RIR）的低频组件（RIR）对于实现良好的替代物很重要。我们使用由以混合方式生成的合成RIR组成的GWA数据集：基于精确的波浪求解器用于模拟较低的频率和几何射线跟踪方法模拟了较高的频率。我们证明，在混合合成RIRS上训练的语音替代模型优于在四个现实世界RIR数据集上通过先前几何射线追踪方法生成的RIR训练的模型。
We present a novel approach to improve the performance of learning-based speech dereverberation using accurate synthetic datasets. Our approach is designed to recover the reverb-free signal from a reverberant speech signal. We show that accurately simulating the low-frequency components of Room Impulse Responses (RIRs) is important to achieving good dereverberation. We use the GWA dataset that consists of synthetic RIRs generated in a hybrid fashion: an accurate wave-based solver is used to simulate the lower frequencies and geometric ray tracing methods simulate the higher frequencies. We demonstrate that speech dereverberation models trained on hybrid synthetic RIRs outperform models trained on RIRs generated by prior geometric ray tracing methods on four real-world RIR datasets.
