论文标题
深度卷积神经网络基于基于语音的反向过滤方法
Deep Convolutional Neural Network-based Inverse Filtering Approach for Speech De-reverberation
论文作者
论文摘要
在本文中，我们引入了一种使用深卷积神经网络（CNN）的单渠道语音进行逆转的频谱域反向滤波方法。主要目标是更好地处理逼真的混响条件，在这种情况下，房间脉冲响应（RIR）过滤器比短时傅立叶变换（STFT）分析窗口更长。为此，我们考虑了回响语音信号的备速传输函数（CTF）模型。在提出的框架中，对CNN体系结构进行了训练，以直接估计CTF模型的反过滤器。在CNN结构的各种选择中，我们考虑的U-NET由具有跳过连接的完全横向自动编码器网络组成。实验结果表明，所提出的方法比在各种混响条件下的普遍基准算法提供了更好的去逆性能。
In this paper, we introduce a spectral-domain inverse filtering approach for single-channel speech de-reverberation using deep convolutional neural network (CNN). The main goal is to better handle realistic reverberant conditions where the room impulse response (RIR) filter is longer than the short-time Fourier transform (STFT) analysis window. To this end, we consider the convolutive transfer function (CTF) model for the reverberant speech signal. In the proposed framework, the CNN architecture is trained to directly estimate the inverse filter of the CTF model. Among various choices for the CNN structure, we consider the U-net which consists of a fully-convolutional auto-encoder network with skip-connections. Experimental results show that the proposed method provides better de-reverberation performance than the prevalent benchmark algorithms under various reverberation conditions.
