An eigenvalue filtering based subspace approach for speech enhancement
An eigenvalue filtering based subspace approach for speech enhancement
复制标题
DOI：
10.3397/1/376305
复制
发表时间：
2015
影响因子：
0.4
通讯作者：
Chengli Sun;Junsheng Mu
中科院分区：
文献类型：
--
作者：
Chengli Sun;Junsheng Mu
本文提出了一种基于子空间特征值滤波的语音增强方法。该方法首先基于广义特征值分解(GEVD)同时对角化干净语音和噪声信号的协方差矩阵，然后对特征值小于零的较小分量进行滤波。由于滤波后的剩余特征向量矩阵是不可逆的，我们引入了广义逆矩阵变换来解决这个问题来恢复语音信号。实验结果表明，该方法在强噪声环境下比传统方法具有更低的残馀噪声和更低的语音失真。
In this paper, a subspace approach based on eigenvalue filtering is proposed for enhancement of corrupted speech. The new method firstly simultaneously diagonalizes the covariance matrix of clean speech and noise signal based on GEVD (generalized eigenvalues decomposition), and then filters the smaller components whose eigenvalues are less than zero. Because the remainder eigenvector matrix after filtering is irreversible, we introduce the generalized inverse matrix transform to solve this problem for recovery of speech signal. Experimental results show the proposed method performs better than many conventional methods under strong noise conditions, in terms of yielding less residual noise and lower speech distortion.