Bistatic Reverberation in Shallow Water：Modelling and Data Comparison

Bistatic and monostatic reverberation data were recorded in the 2001 Asian Sea International Acoustic Experiment (ASLIAEX).A model based on the normal mode theory has been developed to calculate bistatic bottom reverberation in shallow water and to explain the recorded data.The comparisons between the monostatic and bistatic reverberation data are discussed,and the comparisons between model predictions and measured bistatic reverberation data are also presented.The numerical and experimental results show that the numerical predictions from the bistatic reverberation model fit the experimental data well,and the long-range bistatic reverberation with a time delay can be approximately expressed by the monostatic reverberation data.     

Maximum likelihood polynomial regression for robust speech recognition

The linear hypothesis is the main disadvantage of maximum likelihood linear regression (MLLR).This paper applies the polynomial regression method to model adaptation and establishes a nonlinear model adaptation algorithm using maximum likelihood polynomial regression(MLPR)for robust speech recognition.In this algorithm,the nonlinear relationship between training and testing Gaussian means in every Mel channel is approximated by a set of polynomials and the polynomial coefficients are estimated from adaptation data in test environment using the expectation-maximization(EM)algorithm and maximum likelihood(ML) criterion.The experimental results show that the second-order polynomial can approximate the actual nonlinear function better and in noise compensation and speaker adaptation,the word error rates of MLPR are significantly lower than those of MLLR.The proposed MLPR algorithm overcomes the limitation of linear hypothesis well and can decrease the impact of noise,speaker and other factors simultaneously.It is especially suitable for joint adaptation of speaker and noise.     

Characterisation of the two artificial neural plasma density with network models

This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system. In this model, the input layer is composed of five neurons： the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is the target output neuron： the plasma density. The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe. The effectiveness of two artificial neural network models are demonstrated, the results show good agreements with corresponding experimental data. The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded, and the radial based function is more suitable than the multi layer perceptron in this work.     

An improved algorithm for noise-robust sparse linear prediction of speech

The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments.To tackle this issue,an improved noise-robust sparse linear prediction algorithm is proposed.First,the linear prediction residual of speech is modeled as Student-t distribution,and the additive noise is incorporated explicitly to increase the robustness,thus a probabilistic model for sparse linear prediction of speech is built.Furthermore,variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters,and then the optimal linear prediction parameters are estimated robustly.The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l₁ norm minimization based sparse linear prediction algorithm proposed in recent years.Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.     

Stochastic Inversion of Seabottom Scattering Coefficients from Shallow-Water Reverberation

A stochastic inverse method is presented to estimate the seabottom scattering coeffcients on the base of shallow-water ray mode reverberation theory. The reverberation data measured in the Asian Sea International Acoustic Experiment (ASIAEX) in the East China Sea in 2001 are analysed. The seabottom scattering coefficients areestimated from the reverberation loss. The inverted results are then tested by comparing the experiment data with the predictions of the reverberation vertical correlation and the reverberation loss measured on the different sea states at the same site. The fact that the experimental data are in good agreement with the theoretical predictions indicates the validity of the estimates.     

Prediction of the plasma distribution using artificial neural network

In this work,an artificial neural network (ANN) model is established using a back-propagation training algorithm in order to predict the plasma spatial distribution in an electron cyclotron resonance (ECR)—plasma-enhanced chemical vapor deposition (PECVD) plasma system. In our model, there are three layers:the input layer, the hidden layer and the output layer. The input layer is composed of five neurons: the radial position, the axial position, the gas pressure, the microwave power and the magnet coil current. The output layer is our target output neuron: the plasma density. The accuracy of our prediction is tested with the experimental data obtained by a Langmuir probe, and ANN results show a good agreement with the experimental data. It is concluded that ANN is a useful tool in dealing with some nonlinear problems of the plasma spatial distribution.     

A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction

A method of modifying the architecture of fractional least mean square （FLMS） algorithm is presented to work with nonlinear time series prediction. Here we incorporate an adjustable gain parameter in the weight adaptation equation of the original FLMS algorithm and absorb the gamma function in the fractional step size parameter. This approach provides an interesting achievement in the performance of the filter in terms of handling the nonlinear problems with less computational burden by avoiding the evaluation of complex gamma function. We call this new algorithm as the modified fractional least mean square （MFLMS） algorithm. The predictive performance for the nonlinear Mackey glass chaotic time series is observed and evaluated using the classical LMS, FLMS, kernel LMS, and proposed MFLMS adaptive filters. The simulation results for the time series with and without noise confirm the superiority and improvement in the prediction capability of the proposed MFLMS predictor over its counterparts.     

Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction

This paper presents an adaptive step-size modified fractional least mean square （AMFLMS） algorithm to deal with a nonlinear time series prediction. Here we incorporate adaptive gain parameters in the weight adaptation equation of the original MFLMS algorithm and also introduce a mechanism to adjust the order of the fractional derivative adaptively through a gradient-based approach. This approach permits an interesting achievement towards the performance of the filter in terms of handling nonlinear problems and it achieves less computational burden by avoiding the manual selection of adjustable parameters. We call this new algorithm the AMFLMS algorithm. The predictive performance for the nonlinear chaotic Mackey Glass and Lorenz time series was observed and evaluated using the classical LMS, Kernel LMS, MFLMS, and the AMFLMS filters. The simulation results for the Mackey glass time series, both without and with noise, confirm an improvement in terms of mean square error for the proposed algorithm. Its performance is also validated through the prediction of complex Lorenz series.     

Characterisation of the plasma density with two artificial neural network models

This paper establishes two artificial neural network models by using a multi layer perceptron algorithm and radial based function algorithm in order to predict the plasma density in a plasma system.In this model,the input layer is composed of five neurons:the radial position,the axial position,the gas pressure,the microwave power and the magnet coil current.The output layer is the target output neuron:the plasma density.The accuracy of prediction is tested with the experimental data obtained by the Langmuir probe.The effectiveness of two artificial neural network models are demonstrated,the results show good agreements with corresponding experimental data.The ability of the artificial neural network model to predict the plasma density accurately in an electron cyclotron resonance-plasma enhanced chemical vapour deposition system can be concluded,and the radial based function is more suitable than the multi layer perceptron in this work.     

Nonlinear constitutive law for ferroelectric/ferroelastic material and its finite element realization

The hysteresis phenomena of ferroelectric/ferroelastic material in polarization procedure are investigated. Some assumptions are presented based on the published experimental data. The electrical yielding criterion, mechanical yielding criterion and isotropic hardening model are established. The flow theory in incremental forms in polarization procedure is presented. The nonlinear constitutive law for electrical-mechanical coupling is proposed phenomenologically. Finally, the nonlinear constitutive law expressed in a form of matrices and vectors, which is immediately associated with finite element analysis, is formulated. In the example problem of a rectangular specimen subjected to a uniaxial electric field, the procedure from virgin state to fully polarized state is simulated. Afterward, a uniaxial compressive loading is applied to depolarizing the specimen. Results are in agreement with the experimental data.     

水下目标回波与混响的时频形态特征域盲分离

水下沉底或掩埋目标识别受海底混响干扰严重,并且目标回波与混响在时-频域上均存在混叠,增大了对目标回波与混响的分离难度。根据目标回波与混响的产生机理差异,将图像形态学与时频分析相结合的盲分离算法用于目标回波与混响的分离。推导了目标几何声散射成分与混响在Wigner-Ville时频面的形态特征表达式,利用图像形态学滤波去除Wigner-Ville时频面中的混响及刚性亮点之间交叉项,在时频形态特征域获取解混矩阵,实现了目标回波和混响的分离。仿真与实验数据处理结果表明,结合图像形态学的时频域盲分离算法提高了目标回波信号的信混比。      

Fitting data, but poor predictions: reverberation prediction uncertainty when seabed parameters are derived from reverberation measurements

For many decades, researchers have been developing inverse techniques for estimating seabed parameters from reverberation data, notably scattering strength. Generally, the angular dependence of the scattering kernel is unknown and is either solved for or assumed fixed. In either case, agreement is typically quite good between the measured reverberation and that modeled (by fitting scattering parameters). However, what are the resulting uncertainties in a reverberation prediction if the ocean or geometry changes? The main results of the paper are that (1) these prediction uncertainties are surprisingly large, of order 10 dB at 10 km and thus (2) traditional/current methods for reverberation inversion should be augmented, mitigating the large prediction uncertainties by an additional measurement. Several options for additional measurements are discussed.     

非线性内波存在下的浅海远程混响模型

研究了浅海非线性内波对远程混响场的影响。通过分析非线性内波活动引起声源到海底散射元以及散射元到接收点之间的声传播变化,给出了非线性内波引起远程混响强度变化的表示,建立了非线性内波存在下的浅海远程混响模型,数值计算了非线性内波运动引起远程混响强度的变化。理论和数值计算表明,非线性内波的活动能够引起远程混响强度的变化,在某些情况下会导致远程混响强度增强。通过讨论非线性内波引声简正波的耦合效应,给出了其引起远程混响强度增强的原因。      

Estimating the sound absorption coefficients of perforated wooden panels by using artificial neural networks

Developing efficient sound absorption materials is a relevant topic for large scale structures such as gymnasiums, shopping malls, airports and stations. This study employs artificial neural network (ANN) algorithm to estimate the sound absorption coefficients of different perforated wooden panels with various setting combinations including perforation percentage, backing material and thickness. The training data sets are built by carrying out a series of experimental measurements in the reverberation room to evaluate the sound absorption characteristics of perforated wooden panels. A multiple linear regression (MLR) model is also developed for making comparisons with ANN. The analytical results indicate that the ANN exhibits satisfactory reliability of a correlation between estimation and truly measured absorption coefficients of approximately 0.85. However, MLR cannot be applied to nonlinear cases. ANN is a useful and reliable tool for estimating sound absorption coefficients estimation.     

Prediction of reverberation time and speech transmission index in long enclosures

It is known that the sound field in a long space is not diffuse, and that the classic theory of room acoustics is not applicable. A theoretical model is developed for the prediction of reverberation time and speech transmission index in rectangular long enclosures, such as corridors and train stations, where the acoustic quality is important for speech. The model is based on an image-source method, and both acoustically hard and impedance boundaries are investigated. An approximate analytical solution is used to predict the frequency response of the sound field. The reverberation time is determined from the decay curve which is computed by a reverse-time integration of the squared impulse response. The angle-dependence of reflection coefficients of the boundaries and the change of phase upon reflection are incorporated in this model. Due to the relatively long distance of sound propagation, the effect of atmospheric absorption is also considered. Measurements of reverberation time and speech transmission index taken from a real tunnel, a corridor, and a model tunnel are presented. The theoretical predictions are found to agree well with the experimental data. An application of the proposed model has been suggested.     

结合卷积神经网络与混响时间注意力机制的混响抑制

提出一种结合卷积神经网络的编解码器模型和混响时间注意力机制的混响抑制算法,该算法通过编解码器模型实现混响抑制,并利用混响时间注意力机制克服混响环境变化对混响抑制效果的影响。该算法在编码器中使用具有不同大小的卷积核来处理混响语音幅度谱,从而获得包含多尺度上下文信息的编码特征;通过引入注意力模块,实现在不同的混响时间环境中选择性地使用不同权重的编码特征生成加权特征;最后,在解码器中使用加权特征来重建混响抑制后的语音信号幅度谱。在模拟和真实的混响环境下,该算法相对于基线系统在语音混响调制能量比上分别取得了0.36 dB和0.66 dB的提升。实验结果表明,该算法可以适应不同混响环境的变化,相对基线系统在真实混响环境下具有更高的鲁棒性。      

Optimization of constrained composite absorbers using simulated annealing

Sound reverberation is an important problem in some industrial environments. As indicated by the Occupational Safety and Health Act of 1970, noise is responsible for the psychological and physiological ills of workers. Therefore reduction of reverberation becomes essential. For maintenance and other reasons, the thickness of sound absorbers used for reverberation control may be constrained. Consequently there is interest in minimizing noise using sound absorbers with constrained thickness. Optimization of a composite absorber using a simulated annealing algorithm is presented. Simulated annealing is a stochastic relaxation technique based on analogy with the physical process of annealing metal. The algorithm requires a mathematical model for the acoustical properties of the absorber. Before optimization, the accuracy of the mathematical model was checked against experimental data. A program for optimizing in respect of broad band noise at a specified receiver has been created and run. Results prove that SA optimization provides a quick and efficient approach in designing constrained thickness composite sound absorbers.     

应对说话人位置突变的鲁棒语声去混响*

多通道线性预测算法是一种有效的语声去混响方法,但若房间内说话人位置发生变化,房间冲激响应也会随之改变,从而导致去混响效果变差。本文研究了基于递归最小二乘算法的自适应多通道线性预测语声去混响方法,提出了一种去混响过程中检测说话人位置突变的方法,它借助已有的多通道传声器信号,并未明显增加系统开销。实验表明该方法可以有效检测说话人位置的突变,并通过重置滤波器改善算法的收敛速度和稳定性。     

Method of coupled mode for long-range bottom reverberation

The theory of coupled mode is used for modeling the long-range bottom reverberation in shallow water caused by bottom roughness. The distant bottom reverberation level and spatial coherence of impulsive source are both derived. The results agree with those from the classical reverberation model, and are compared with the experimental data. The influence of source bandwidth and the distance between sources and receivers on the intensity of bottom reverberation are particularly discussed. The method is shown to be available for both the monoand the bi-static cases.     

混响室雷达地杂波统计特性模拟

为实现混响室中威布尔分布杂波的有效模拟,营造逼真的雷达电磁环境,基于广义平稳非相关散射电磁环境下混响室信道衰落特性,通过控制幅度调制信号对衰落系数进行补偿,提出一种基于时域波形设计的混响室地杂波模拟方法。通过调节输入信号序列中大、小幅度脉冲比例,改变混响室传统输入信号的零均值幅度特性,以实测地杂波统计特性为参考,最终得到与实测数据统计特性参数一致的混响室杂波电磁环境。用最大似然估计和KS检验法对实验数据作参数估计和假设检验,实测地杂波数据拟合于标准威布尔模型,混响室实验数据拟合于实测地杂波幅度统计模型,从而实现了实测地杂波起伏特性的混响室有效模拟。