摘要
MCRA最小值递归平均算法对噪声的估计值较为准确,而且对一段话音内噪声功率谱的变化也能准确的追踪.但是面对噪声功率谱突然陡增这种情况,需要经过一段时间的自适应才能得到准确的噪声估计值,而在这个自适应期间,会留下较强的残留噪声,影响人的听感.本文在MCRA算法的基础上,引入一种利用最大对数似然比结合能零比的VAD(Voice activity Detection)辅助算法,得到一种改进型噪声估计算法.实验仿真结果也表明,改进的噪声估计算法在噪声估计速度方面优于MCRA算法.
The MCRA minimum recursive algorithm is accurate for the noise estimation, and the changes of noise power spectrum in a speech can be tracked accurately. However, if the noise power spectrum increases too much suddenly, the original algorithm needs a period of time to get the accurate noise, and in this adaptive period, it will leave strong residual noise and affect people＇s hearing experience. This paper introduces a Voice Activity Detection（VAD） algorithm which uses the maximum log-likelihood ratio with energy-zero ratio, and an improved noise estimation algorithm on the basis of MCRA is obtained. Experimental simulation also proves that the improved algorithm is better than the original algorithm in noise estimation speed.
作者
胡岸
高勇
HU An;GAO Yong(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China)
出处
《计算机系统应用》
2018年第9期124-129,共6页
Computer Systems & Applications
关键词
语音增强
最大对数似然比
能零比
噪声估计
MCRA
speech enhancement
maximum logarithmic likelihood ratio
energy-zero ratio
noise estimation
MCRA
