乘性色噪声和加性双值噪声作用下延迟双稳系统中的随机共振

研究了在乘性和加性色噪声以及加性非对称双值噪声作用下方波信号驱动的延迟双稳系统中的随机共振现象。基于小延迟近似,在绝热近似条件下推导出了系统输出信噪比(Signal-to-Noise Ratio, SNR)的解析形式。分析结果表明,随着双值噪声强度与非对称性参数、方波信号幅度以及乘性和加性色噪声强度的变化,SNR表现出了随机共振行为。随着延迟时间、色噪声相关时间和系统参数的增大,SNR作非单调变化。     

阈值阵列模型与超阈值随机共振

研究了随机共振的一个特殊形式——超阈值随机共振。超阈值随机共振是基于阈值阵列模型的随机共振现象。分析了阈值阵列模型输出随机过程的统计特性,固定输入信噪比,可观察输出信噪比增益随阈值噪声方差的改变产生非单调变化的规律。实验验证了含噪周期输入信号经阈值阵列系统,在统计独立、服从高斯分布的阈值噪声作用下,输出信号信噪比增益大于1。且对于非高斯噪声,会获得更高的输出信噪比。     

电报噪声作用下线性系统的随机共振

研究了当系统的阻尼率、固有频率和激励信号受色电报作用时,二阶过阻尼线性系统中的随机共振现象。通过线性系统理论和相关删去法,得到了系统平均输出幅度增益的精确表达式。系统平均输出幅度增益是电报噪声的强度、相关率、系统的阻尼率、固有频率以及激励信号的频率的非单调函数;适当的噪声参数和系统参数可以使有噪声情况下的输出幅度增益大于无噪声时的输出幅度增益;通过改变噪声参数和系统参数可以提高系统的输出幅值增益。此方法适用于电报噪声环境中线性系统的微弱信号检测。     

基于变参数随机共振和归一化变换的时变信号检测与恢复

非线性随机共振系统具有利用噪声增强微弱信号的能力,为强噪声背景下的信号检测开辟了新的途径。该文提出一种变参数随机共振(VPSR)模型,实现对非周期信号的有效检测、噪声去除和信号恢复。通过以恢复信号的拟合决定系数和互相关系数作为评判标准,研究分析了不同参数变化对系统输出的影响,分析结果表明该模型能有效地从噪声背景中恢复时变信号。该方法拓展了随机共振用于时变信号检测技术的领域,在时变信号检测和处理以及雷达通讯等方向有着一定的潜在应用价值。     

弱信号检测领域中随机共振的理论及应用研究

随机共振(SR)是指在一定的非线性条件下,由弱周期信号和噪声(随机干扰)合作而导致的系统强周期输出的现象.介绍了随机共振的起源和绝热近似理论、本征值理论、非周期理论、自适应理论等随机共振理论;提出了实验研究中常用的测度方法及指标,并介绍了近年来国内外各个领域利用随机共振检测弱信号的应用研究实例.最后对随机共振的研究进展做了概括分析.     

周期脉冲序列在阈值系统中随机共振的研究

将混有加性高斯白噪声的周期脉冲序列通过阈值系统,对其传输性质进行理论研究.以输入-输出的信号幅度和信噪比增益来分析随机共振现象,通过调节各相关参数使系统、信号和噪声因协同作用而产生随机共振现象:在某一最优噪声强度下获得最大的信号幅度及信噪比的增益.     

离散时间系统中的噪声辅助信号传输

 基于一种平均信噪比讨论了离散时间系统中的三种典型噪声辅助信号传输的问题.仿真得到:当输入周期信号在阈下时,噪声能辅助信号的传输,随机谐振现象存在.存在一个噪声强度范围,其间系统的输出平均信噪比大于其输入平均信噪比,即输出输入平均信噪比增益大于1.随机谐振的功效随着系统阈值的增加,或噪声概率密度函数的拖尾变厚、零均值周边脉冲变窄而降低.这些结果同样说明了离散时间系统中随机谐振的复杂性,它的存在和功效也依赖于系统性能的测度和噪声类型.这些结果拓广了随机谐振在数字信号处理中的应用.     

基于随机共振检测的神经元非周期激励响应模拟研究

非周期激励响应对于神经系统的研究具有重要理论意义和应用价值.为了模拟生物感觉系统中检测信号的机制,基于随机共振检测原理,采用互信息率评价方法,对Hodgkin-Huxley单神经元非周期激励响应进行了研究.实验结果表明,Hodgkin-Huxley单神经元不但阈值下存在非周期随机共振现象,在某些特定的条件下,阈值上也同样存在看非周期随机共振现象.由于噪声广泛存在于信号之中,由此,为研究强噪声背景下的神经系统信号检测提供了一种新的方法.     

关联噪声和周期信号驱动的非对称双稳系统的稳态分析

运用Liouville equation和诺维科夫原理,解出了关联噪声和周期信号共同驱动的非对称双稳系统的近似福克-普朗克方程,并求解了其稳态概率密度函数.在此基础上,分析了乘性噪声强度 、加性噪声强度 、噪声间关联系数 ,周期信号振幅 、频率 以及系统非对称参数 等对稳态概率密度分布曲线的影响.结果表明:(1)噪声强度及其关联、周期信号振幅、系统非对称参数的改变均能引起稳态概率密度分布曲线单峰结构和双峰结构之间的转换,即能够诱导非平衡相变产生;(2)周期信号频率改变时,没有非平衡相变发生;(3)当系统非对称参数为零时,稳态概率密度分布曲线具有关于 的对称结构;当系统非对称参数不等于零时,其对称结构被破坏.     

基于分段线性随机共振模型的信号检测电路研究

提出了一种新的分段线性随机共振模型,建立了模型的数学关系,通过强噪声背景下微弱周期信号检测的数值仿真,验证了模型的有效性.根据该模型设计了硬件电路,对不同噪声强度背景下微弱周期信号以及无噪声周期信号进行了随机共振实验研究和对比分析,结果表明,该电路可以实现对强噪声背景下的微弱周期信号检测,并能显著增强输出信噪比.     

On an aperiodic stochastic resonance signal processor and its application in digital watermarking

In this paper, an aperiodic stochastic resonance (ASR) signal processor for communication systems based on a bistable dynamic mechanism is proposed for detecting base-band binary pulse amplitude modulation (PAM) signals in communication systems. All parameters in the processor can be adjusted when needed. The adjustment mechanism is explained from the perspective of the conventional noise-induced nonlinear signal processing. To demonstrate this processor's usability, a digital image-watermarking algorithm in the discrete cosine transform (DCT) domain is implemented. In this algorithm, the watermark and the DCT alternating current (ac) coefficients of the image are viewed as the input signal and the channel noise, respectively. In conventional watermarking systems, it is difficult to explain why the detection bit error ratio (BER) of a watermarking system suffering from certain attacks is lower than that of the system not suffering from any attack. In the new watermarking algorithm, this phenomenon is systematically analyzed. It is shown that the DCT ac coefficients of an image as well as the noise imposed by the attacks can cooperate within the bistable system to improve the performance of the watermark detection.     

Stochastic resonance in discrete time nonlinear AR(1) models

This paper deals with stochastic resonance. This nonlinear physical phenomenon generally occurs in bistable systems excited by random input noise plus a sinusoid. Through its internal dynamics, such a system forces cooperation between the input noise and the input sine: provided the existence of fine tuning between the power noise and the dynamics, the system reacts periodically at the frequency of the sine. Of particular interest is the fact that the local output signal-to-noise ratio presents a maximum when plotted against the input noise power; the system resounds stochastically. Continuous-time systems have already been studied. We study the ability of intrinsically discrete-time systems [general nonlinear AR(1) models] to produce stochastic resonance. It is then suggested that such discrete systems can be used in signal processing     

阵列双稳随机共振在微弱信号检测中的应用研究

阵列双稳随机共振(stochastic resonance, SR)系统可利用噪声在单个双稳SR系统基础上进一步增强微弱信号检测的能力,为强噪声背景下微弱信号的检测开创了新方法。本文应用阵列双稳SR原理进行微弱信号检测的研究,采用理论和数值仿真相结合,通过稳态自协方差函数,分析了阵列双稳SR系统输出信噪比（signal-to-noise ratio, SNR）增益。在此基础上,分别讨论了阵列噪声、外部噪声及阵列单元数对检测性能的影响。并与单个双稳SR检测弱信号进行性能比较,分析和仿真结果都表明,在相同条件下,采用阵列双稳SR比采用单个双稳SR检测微弱信号性能有较大改善。这些研究结果对于阵列双稳SR的进一步发展及应用具有重要意义。      

基于随机共振的微弱信号检测研究

微弱信号是淹没在噪声中的小信号,且一般其信噪比比较低。微弱信号的检测在物理、电子和生物医学方面都具有重要的意义。依据随机共振理论,噪声在一定的条件下有利于微弱信号的检测。研究了随机共振的原理、双稳态系统中的随机共振现象及随机共振的应用研究现状。     

Multiexpert automatic speech recognition using acoustic and myoelectric signals

Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelectric signals (MES). A new method of combining experts, known as the plausibility method, is employed to combine an acoustic ASR expert and a MES ASR expert. The plausibility method of combining multiple experts, which is based on the mathematical framework of evidence theory, is compared to the Borda count and score-based methods of combination. Acoustic and facial MES data were collected from 5 subjects, using a 10-word vocabulary across an 18-dB range of acoustic noise. As expected the performance of an acoustic expert decreases with increasing acoustic noise; classification accuracies of the acoustic ASR expert are as low as 11.5%. The effect of noise is significantly reduced with the addition of the MES ASR expert. Classification accuracies remain above 78.8% across the 18-dB range of acoustic noise, when the plausibility method is used to combine the opinions of multiple experts. In addition, the plausibility method produced classification accuracies higher than any individual expert at all noise levels, as well as the highest classification accuracies, except at the 9-dB noise level. Using the Borda count and score-based multiexpert systems, classification accuracies are improved relative to the acoustic ASR expert but are as low as 51.5% and 59.5%, respectively.     

Stochastic Resonance in Bistable System Subject to Multiplicative and Additive Noise

In this paper, a simulation model of bistable system subject to multiplicative and additive noise is built on the basis of the theory of stochastic resonance(SR). SR phenomenon appears in the system subject to multiplicative and additive noise when a single signal transmits in the system. The output waveforms and the power spectrums at different frequencies are compared. The impact of the intensity of multiplicative and additive noise on the bistable system is discussed. It is found that this simulation model can upgrade the quality of the signal processing and the noise intensity can be effectively used for improving the effect of SR.     

基于倒谱特征的带噪语音端点检测

在语音识别系统中产生错误识别的原因之一是端点检测有误差.在高信噪比情况下,正确地确定语音的端点并不困难.然而,大多数实际的语音识别系统需工作在低信噪比情况下,一些常规的端点检测方法,例如基于能量的端点检测方法在噪声环境下不能有效地工作.本文利用倒谱特征来检测语音端点,提出了带噪语音端点检测的两个算法,第一个算法利用倒谱距离代替短时能量作为判决的门限,第二个算法改进了基于隐马尔柯夫模型(HMM)的语音检测以适应噪声的变化,实验结果表明本方法可得到高正确率的带噪语音端点检测.     

Development of an Optimized Feature Extraction Algorithm for Throat Signal Analysis

In this paper, we present a speech recognition system using a throat microphone. The use of this kind of microphone minimizes the impact of environmental noise. Due to the absence of high frequencies and the partial loss of formant frequencies, previous systems using throat microphones have shown a lower recognition rate than systems which use standard microphones. To develop a high performance automatic speech recognition (ASR) system using only a throat microphone, we propose two methods. First, based on Korean phonological feature theory and a detailed throat signal analysis, we show that it is possible to develop an ASR system using only a throat microphone, and propose conditions of the feature extraction algorithm. Second, we optimize the zero‐crossing with peak amplitude (ZCPA) algorithm to guarantee the high performance of the ASR system using only a throat microphone. For ZCPA optimization, we propose an intensification of the formant frequencies and a selection of cochlear filters. Experimental results show that this system yields a performance improvement of about 4% and a reduction in time complexity of 25% when compared to the performance of a standard ZCPA algorithm on throat microphone signals.     

Spoken language understanding

Semantics deals with the organization of meanings and the relations between sensory signs or symbols and what they denote or mean. Computational semantics performs a conceptualization of the world using computational processes for composing a meaning representation structure from available signs and their features present, for example, in words and sentences. Spoken language understanding (SLU) is the interpretation of signs conveyed by a speech signal. SLU and natural language understanding (NLU) share the goal of obtaining a conceptual representation of natural language sentences. Specific to SLU is the fact that signs to be used for interpretation are coded into signals along with other information such as speaker identity. Furthermore, spoken sentences often do not follow the grammar of a language; they exhibit self-corrections, hesitations, repetitions, and other irregular phenomena. SLU systems contain an automatic speech recognition (ASR) component and must be robust to noise due to the spontaneous nature of spoken language and the errors introduced by ASR. Moreover, ASR components output a stream of words with no structure information like punctuation and sentence boundaries. Therefore, SLU systems cannot rely on such markers and must perform text segmentation and understanding at the same time.     

基于双稳态系统二值图像质量改善算法

针对二值图像处理,为抑制噪声改善图像质量,论文提出一种利用双稳态随机共振系统实现含噪二值图像恢复方法。论文首先对图像分别按行和列重采样为一维信号,并将该一维信号输入双稳态系统,通过欧拉法或龙格-库塔法迭代求解双稳态方程的输出序列,得到恢复图像对行和列两个方向的偏导数,再结合该偏导数根据截断的二维泰勒公式推导出邻近像素点的值,然后对每个像素计算获得的多个值加权平均得到恢复的灰度图像,对灰度图像二值分割即得到最终的复原二值图像。实验结果表明,论文算法能有效从高强度噪声中恢复原二值图像,且相对于传统维纳滤波算法峰值信噪比提高了22.36%,相对于对比算法,峰值信噪比平均提高了10.8%。