基于F范数的信号子空间维度估计的多通道语音增强算法

A signal subspace dimension estimator based on F-norm with application to subspace-based multi-channel speech enhancement

尽管信号子空间方法在语音增强中的应用已经得到了广泛的研究,但是作为制约子空间方法性能的子空间维度估计却一直没有得到较好的解决。针对子空间维度估计问题,本文用多通道语音信号互功率谱矩阵的F范数的统计模型来描述语音信号的先验知识和变化规律,提出了一种基于最大化原则的子空间维度估计方法,在接受原假设的前提下最大化子空间维度。实验证明,在客观语音质量评估和主观测评中,所提算法都取得了更好的结果。与传统方法相比,采用本文方法的多通道语音增强算法可在房间回声、低信噪比等恶劣环境下获得更高的噪声消除和更低的语音畸变。

Although the Signal Subspace Approach（SSA）has been studied extensively for speech enhancement,no good solution has been found to identify signal subspace dimension.In this paper we present a novel signal subspace dimension estimator based on F-norm,with which subspace-based multi-channel speech enhancement is robust to adverse acoustic environments such as room reverberation and low input SNR.The results of experiments show the presented method leads to more noise reduction and less speech distortion comparing with traditional methods.