基于神经元阈上非周期随机共振机制的语音复原技术研究  

Speech Restoration Based on Suprathreshold Stochastic Resonance of Neuron Model

作　　者：薛凌云[1] 向学勤[2] 范影乐[2] 段会龙[1] 

机构地区：[1]浙江大学生物医学工程教育部重点实验室,杭州310027 [2]杭州电子科技大学生物医学工程与仪器研究所,杭州310018

出　　处：《传感技术学报》2009年第2期213-218,共6页Chinese Journal of Sensors and Actuators

基　　金：国家自然科学基金资助项目(60302027);浙江省教育厅科研计划资助项目(20030620)

摘　　要：应用随机共振机制,通过噪声能量来加强语音信号,改善低信噪比语音的输出质量。对FitzHugh-Nagumo(FHN)神经元模型中存在的阈上非周期随机共振现象进行了分析,根据其阈值特性,此二维神经元模型可被等价为两状态的阈值跨越非线性动力学系统。因此对含噪语音信号添加噪声,产生阈值化后的二值输出,经迭代收敛进入阈上非周期随机共振状态。在一个非零添加噪声强度上,含噪语音输出的互相关系数将达到最大值。通过语音复原的结果表明,本文方法对噪声的变化有更好的鲁棒性,尤其在强背景噪声下,随机共振方法较其他传统方法有更佳的复原效果。The quality of low signal-noise-ratio speech signal was improved by adding the noise energy to speech signal based on stochastic resonance mechanism. The suprathreshold stochastic resonance phenomenon of non-periodic response of FitzHugh-Nagumo （FHN） neuron model was analyzed firsdy. According to the threshold property, this two-dimensional neuron model may be equivalent to one nonlinear dynamic system, which has two states under the threshold condition. Adding the noise to the corrupted speech sig- nal, the two-value output was produced. The system was driven to the state of non-periodic suprathreshold stochastic resonance after the iterative convergence. The mutual relation ratio of the speech output will reach the maximum by adding a non-zero noise. The speech recovery results show that this method has a better robustness, particularly in strong background noise, stochastic resonance method than other conventional methods has a better recovery result.

关 键 词：随机共振 语音复原FitzHugh-Nagumo神经元 

分 类 号：TN912.3[电子电信—通信与信息系统]