基于自适应阈值活动语音检测和最小均方误差对数谱幅度估计的低信噪比降噪算法  

Low SNR denoising algorithm based on adaptive voice activity detection and minimum mean-square error log-spectral amplitude estimation

作　　者：张皓然 王学渊[1] 李小霞[1] ZHANG Haoran;WANG Xueyuan;LI Xiaoxia(Sichuan Key Laboratory of Special Environmental Robotics(School of Information Engineering,Southwest University of Science and Technology),Mianyang Sichuan 621010,China)

机构地区：[1]特殊环境机器人技术四川省重点实验室(西南科技大学信息工程学院),四川绵阳621010

出　　处：《计算机应用》2020年第6期1763-1768,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61771411)。

摘　　要：针对低信噪比(SNR)环境下传统方法对声信号降噪的局限性,提出了一种联合自适应阈值活动语音检测(VAD)算法和最小均方误差对数谱幅度估计(MMSE-LSA)的实时降噪算法。首先,在VAD算法中通过基于能量概率最大值的概率统计来对背景噪声进行估计,对得到的背景噪声进行实时更新并保存;然后,将实时更新的背景噪声作为MMSE-LSA的参考噪声,并对噪声幅度谱进行自适应更新,最后进行降噪处理。通过在真实场景中对四类声信号进行实验,结果表明,该算法在保证对低SNR声信号的实时处理的情况下,相较于传统MMSE-LSA算法,降噪信号的SNR能够提高10~15 dB,且不存在信号过减的情况,可应用于实际工程。Aiming at the limitations of traditional noise reduction methods for acoustic signals in low Signal-to-Noise Ratio(SNR)environment,a real-time noise reduction algorithm was proposed by combining adaptive threshold Voice Activity Detection(VAD)algorithm and Minimum Mean-Square Error Log-Spectral Amplitude estimation(MMSE-LSA).Firstly,the background noise was estimated in VAD algorithm by probability statistics based on the maximum value of the energy probability,and the obtained background noise was updated in real time and saved.Then,the background noise updated in real time was used as the reference noise of MMSE-LSA,and the noise amplitude spectrum was updated adaptively.Finally,the noise reduction processing was performed.The experimental results on four kinds of acoustic signals in real scenes show that the proposed algorithm can guarantee the real-time processing of low SNR acoustic signals;and compared with the traditional MMSE-LSA algorithm,it has the SNR of the noise reduction signal increased by 10-15 dB without over-subtraction.It can be applied to practical engineering.

关 键 词：真实环境 自适应阈值 活动语音检测算法 实时最小均方误差对数谱幅度估计算法 实时背景 低信噪比 

分 类 号：TP912.35[自动化与计算机技术]