Lu Huanhuan, Wang Fuzhong, Zhang Huichun
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2016 Apr;33(2):357-61.
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.
传统的语音检测方法将噪声视为干扰信号进行滤波，但在强噪声背景下，这些方法在消除噪声的同时会丢失部分原始语音信号。随机共振可以利用噪声能量来放大微弱信号并抑制噪声。根据随机共振理论，提出了一种基于自适应随机共振的微弱语音信号提取新方法。该方法结合两次采样，实现了从强噪声中检测微弱语音信号。通过评估输出信号的信噪比自适应调整系统参数a、b，进而实现对微弱语音信号的最优检测。实验仿真分析表明，在强噪声背景下，输出信噪比从初始值-7dB提高到约0.86dB，信噪比增益为7.86dB。该方法显著提高了输出语音信号的信噪比，为在强噪声环境下检测微弱语音信号提供了新思路。
