基于自适应随机共振理论强噪声背景下的弱语音信号检测

Detection of Weak Speech Signals from Strong Noise Background Based on Adaptive Stochastic Resonance

传统语音信号检测方法是将噪声作为干扰信号来线性滤除,而在强噪声背景下,这些方法在去除噪声的同时也丢失了部分原始语音信号。随机共振能够利用噪声能量放大弱信号而抑制噪声,基于此原理,提出一种基于自适应随机共振提取弱语音信号的方法,并与二次采样相结合,实现强噪声背景下弱语音信号的检测。该方法通过评价系统输出信号的信噪比,自适应调节系统参数a、b,从而最优地检测出弱语音信号。实验仿真分析表明,强噪声背景下,输出信号的信噪比由初始信噪比-7dB提高到0.86dB,信噪比增益为7.86dB。该方法明显提高了输出语音信号的信噪比,为弱语音信号的检测提供了新的思路。

Traditional speech detection methods regard the noise as a jamming signal to filter,but under the strong noise background,these methods lost part of the original speech signal while eliminating noise.Stochastic resonance can use noise energy to amplify the weak signal and suppress the noise.According to stochastic resonance theory,a new method based on adaptive stochastic resonance to extract weak speech signals is proposed.This method,combined with twice sampling,realizes the detection of weak speech signals from strong noise.The parameters of the systema,b are adjusted adaptively by evaluating the signal-to-noise ratio of the output signal,and then the weak speech signal is optimally detected.Experimental simulation analysis showed that under the background of strong noise,the output signal-to-noise ratio increased from the initial value-7dB to about 0.86 dB,with the gain of signalto-noise ratio is 7.86 dB.This method obviously raises the signal-to-noise ratio of the output speech signals,which gives a new idea to detect the weak speech signals in strong noise environment.