摘要
本文讨论的语声信息恢复旨在提高带噪语声的可懂度。通过类比听觉掩蔽与视觉闭塞,在基于稀疏表示的图像去噪思想启发下,本文提出了基于压缩感知理论的稀疏表示语声恢复模型、数学表达式以及算法。与传统的语声增强算法不同,本文模型与算法的特点在于具备有效消除全局噪声干扰和恢复局部被噪声掩蔽的语声成分的双重能力,有效提高了处理后语声的可懂度。仿真实验和客观语声质量测度验证了提出的模型与算法的可行性、有效性以及优越性。
The speech recovery discussed by this paper aims at improving the intelligibility of noisy speech.By comparing of auditory masking and vision occlusion and inspired by image denoising idea base on sparse representation,this paper proposes a speech recovery model over sparse representation based on compressive sensing theory,its mathematical expression and the algorithm.Different from traditional speech enhancement algorithms,the superiority of this model and algorithm lies in its twofold ability of effectively eliminating global noise interference and recovering local incomplete speech components masked by noise interference,which improves the intelligibility of processed speech.Simulation experiments and objective speech quality measures verify that the proposed model and algorithm are feasible,effective and superior.
出处
《信号处理》
CSCD
北大核心
2014年第8期914-923,共10页
Journal of Signal Processing
关键词
语声恢复
压缩感知
稀疏表示
speech recovery
compressive sensing
sparse representation
