摘要
MCRA(Minima-Controlled Recursive Averaging)方法是经典的噪声估计算法,然而在语音段MCRA方法存在不能对噪声功率谱进行有效更新的问题.针对这一问题,本文利用广义自回归条件异方差(Generalized Autoregressive Conditional Heteroskedasticity,GARCH)模型在时频域对噪声信号建模,在MCRA算法原理的基础上,提出了基于最小控制GARCH模型的噪声估计算法,实验结果表明,本文所提的噪声估计算法能够更为准确估计噪声功率谱,将该算法应用到语音增强中能够获得到较好的语音增强效果.
Considering the problem that the typical M CRA（ M inima-Controlled Recursive Averaging） noise estimate algorithm fails to update the pow er spectrum of noise effectively w hen the speech is present,so this paper proposes a noise estimate algorithm based on minima controlled GARCH model. The noise signal is modeled as a GARCH process in timefrequency domain and then the proposed noise estimate algorithm is achieved combined w ith the basis of the framew ork of M CRA method. Experimental and testing results indicate that the proposed algorithm can estimate the spectrum of noise more accurately compared w ith the reference methods. When the proposed algorithm is applied into speech enhancement,a better performance can be achieved as w ell.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2016年第3期747-752,共6页
Acta Electronica Sinica
基金
国家自然科学基金(No.61471014)
