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

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.