Linear Multilayer ICA Using Adaptive PCA

Linear multilayer independent component analysis (LMICA) is an approximate algorithm for ICA. In LMICA, approximate independent components are efficiently estimated by optimizing only highly dependent pairs of signals when all the sources are super-Gaussian. In this paper, the nonlinear functions in LMICA are generalized, and a new method using adaptive PCA is proposed for the selection of pairs of highly dependent signals. In this method, at first, all the signals are sorted along the first principal axis of their higher-order correlation matrix. Then, the sorted signals are divided into two groups so that relatively highly correlated signals are collected in each group. Lastly, each of them is sorted recursively. This process is repeated until each group consists of only one or two signals. Because a well-known adaptive PCA algorithm named PAST is utilized for calculating the first principal axis, this method is quite simple and efficient. Some numerical experiments verify the effectiveness of LMICA with this improvement.     

基于线性预测的自适应递归最小二乘盲源分离

为了分离具有时序结构的信号,将线性预测均方误差作为代价函数.使分离出信号的可预测性最大,这样就可以分离出源信号.这种最小均方误差型算法,其在线形式采用瞬时预测误差代替预测误差的期望值.导致收敛速度较慢.为了提高这类算法的收敛速度,本文将线性预测误差的加权平均作为代价函数,提出了递归最小二乘型线性预测盲源分离算法.计算机仿真和实际语音分离试验均表明:提出的算法与最小均方误差型线性预测盲源分离算法相比具有更快的收敛速度,且增加的计算量不大.     

Dereverberation and Denoising Using Multichannel Linear Prediction

Reverberation in a room severely degrades the characteristics and auditory quality of speech captured by distant microphones, thus posing a severe problem for many speech applications. Several dereverberation techniques have been proposed with a view to solving this problem. There are, however, few reports of dereverberation methods working under noisy conditions. In this paper, we propose an extension of a dereverberation algorithm based on multichannel linear prediction that achieves both the dereverberation and noise reduction of speech in an acoustic environment with a colored noise source. The method consists of two steps. First, the speech residual is estimated from the observed signals by employing multichannel linear prediction. When we use a microphone array, and assume, roughly speaking, that one of the microphones is closer to the speaker than the noise source, the speech residual is unaffected by the room reverberation or the noise. However, the residual is degraded because linear prediction removes an average of the speech characteristics. In a second step, the average of the speech characteristics is estimated and used to recover the speech. Simulations were conducted for a reverberation time of 0.5 s and an input signal-to-noise ratio of 0 dB. With the proposed method, the reverberation was suppressed by more than 20 dB and the noise level reduced to -18 dB.     

自适应网络流量线性预测算法及应用*

Internet网络流量的分析、模型仿真以及流量的预测,在网络管理和设计中起着很重要的作用。分析了CERNET网络流量行为,提出了CERNET IP Backbone的流量模型,同时将自适应滤波的新思想引入网络流量的模型仿真和预测,提出了自适应网络流量线性预测的新算法,并将其应用于CERNET的网络流量预测。     

基于双边线性预测的自适应时频分析

针对非平稳信号的时频分析,采用所谓的双边线性预测方法代替传统的单边线性预测。在双边线性预测中,采用若干过去值和将来值的线性组合形式来对信号建模;采用基于最小二乘的噪声梯度法来估计预测系数向量。数值仿真结果表明,双边线性预测方法比单边方法具有更高的分辨率,同时所需要的阶次比较小,因而是一种有效的计算方法。     

Precise Dereverberation Using Multichannel Linear Prediction

In this paper, we discuss the numerical problems posed by the previously reported LInear-predictive Multi-input Equalization (LIME) algorithm when dealing with dereverberation of long room transfer functions (RTF). The LIME algorithm consists of two steps. First, a speech residual is calculated using multichannel linear prediction. The residual is free from the room reverberation effect but it is also excessively whitened because the average speech characteristics have been removed. In the second step, LIME estimates such average speech characteristics to compensate for the excessive whitening. When multiple microphones are used, the speech characteristics are common to all microphones whereas the room reverberation differs for each microphone. LIME estimates the average speech characteristics as the characteristics that are common to all the microphones. Therefore, LIME relies on the hypothesis that there are no zeros common to all channels. However, it is known that RTFs have a large number of zeros close to the unit circle on the z-plane. Consequently, the zeros of the RTFs are distributed in the same regions of the z-plane and, if an insufficient number of microphones are used, the channels would present numerically overlapping zeros. In such a case, the dereverberation algorithm would perform poorly. We discuss the influence of overlapping zeros on the dereverberation performance of LIME. Spatial information can be used to deal with the problem of overlapping zeros. By increasing the number of microphones, the number of overlapping zeros decreases and the dereverberation performance is improved. We also examine the use of cepstral mean normalization for post-processing to reduce the remaining distortions caused by the overlapping zeros     

基子自适应泸波器理论的自相似流量预测

根据流量特徵预测其到达速率是基于测量的网络控制机制的关键问题。本文研究了基于最小均方（Least Mean-Square，LMS）自适应滤波器对自相似流量进行速率预测的方法。通过分析对不同实际流量记录和仿真流量的预测结果，发现该方法不但减小了采用指数加权平均估计带来的计算复杂度，而且其滤波器系数自适应特性可以有效地跟踪流量的高度变化，从而更加准确地估计流量速率。此外，实验还得出了用LMS自适应滤波器进行流量预测的几个基本参数。     

基于自适应滤波理论的网络流量线性预测

Internet网络流量的分析、模型仿真以及流量的预测,在网络管理和设计中起着很重要的作用。该文在此方面做了一些工作和尝试,主要有两方面的贡献：（1）在分析和比较了不同模型性能的基础上,提出了CERNET IP backbone的流量模型;（2）将自适应滤波的新思想引入网络流量的模型仿真和预测,提出了自适应网络流量线性预测的新算法。     

基于MATLAB的线性神经网络自适应预测的实现

设计了一个线性神经网络 ,并对其系统进行自适应预测。通过利用 MATL AB6 .5中的神经网络函数adapt对线性网络的自适应训练 ,网络能够随着被预测的模型的变化而相应地对网络的权值和阈值进行修正 ,从而实现对它的自适应预测     

Cascade Prediction Filters With Adaptive Zeros to Track the Time-Varying Resonances of the Vocal Tract

In this paper, a simple and reliable technique is proposed to track vocal tract resonances in continuous speech. The approach is based on the use of predictor filters with adaptive zeros whose constrained trajectories guarantee the successful tracking of the frequency and the damping of each resonance. The zeros are adapted using a gradient-based algorithm to minimize an instantaneous prediction residual according to the principle of minimal disturbance yielding an adaptive structure capable of tracking fast-changing resonance parameters.     

Adaptive Kalman Filtering and Smoothing for Tracking Vocal Tract Resonances Using a Continuous-Valued Hidden Dynamic Model

A novel Kalman filtering/smoothing algorithm is presented for efficient and accurate estimation of vocal tract resonances or formants, which are natural frequencies and bandwidths of the resonator from larynx to lips, in fluent speech. The algorithm uses a hidden dynamic model, with a state-space formulation, where the resonance frequency and bandwidth values are treated as continuous-valued hidden state variables. The observation equation of the model is constructed by an analytical predictive function from the resonance frequencies and bandwidths to LPC cepstra as the observation vectors. This nonlinear function is adaptively linearized, and a residual or bias term, which is adaptively trained, is added to the nonlinear function to represent the iteratively reduced piecewise linear approximation error. Details of the piecewise linearization design process are described. An iterative tracking algorithm is presented, which embeds both the adaptive residual training and piecewise linearization design in the Kalman filtering/smoothing framework. Experiments on estimating resonances in Switchboard speech data show accurate estimation results. In particular, the effectiveness of the adaptive residual training is demonstrated. Our approach provides a solution to the traditional "hidden formant problem," and produces meaningful results even during consonantal closures when the supra-laryngeal source may cause no spectral prominences in speech acoustics     

基于类推和灰色模型的软件阶段成本预测

准确预测软件成本是软件工程领域最具挑战性的任务之一。软件开发固有的不确定性和风险性,使得仅仅在项目早期预测总成本是不够的,还需要在开发过程中持续预测各个阶段的成本,并根据变化趋势重新分配资源,以确保项目在规定的时间和预算内完成。由此,提出一种基于类推和灰色模型的软件阶段成本预测方法——AGSE(Analogy & Grey Model Based Software Stage Effort Estimation)。该杂交方法通过合并两种方法的预测值得到最终的预测结果,避免了单独使用其中一种方法预测时存在的局限性。在真实的软件项目数据集上的实验结果表明,AGSE的预测精度优于类推方法、GM(1,1)模型、GV方法、卡尔曼滤波和线性回归,显示出较大的潜力。     

Robust Adaptive Backstepping Motion Control of Linear Ultrasonic Motors Using Fuzzy Neural Network

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an X-Y-Theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. First, an X-Y-Theta motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which includes cross-coupled interference and friction force, is derived. Moreover, a conventional backstepping approach is proposed to compensate the uncertainties occurred in the motion control system. Furthermore, to improve the control performance in the tracking of the reference contours, an RAFNN backstepping control system is proposed to remove the chattering phenomena caused by the sign function in the backstepping control law. In the proposed RAFNN backstepping control system, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed to estimate the lumped uncertainty directly and a compensator is utilized to confront the reconstructed error of the SAFNN. In addition, the motions at the X axis, Y axis, and Theta axis are controlled separately. The experimental results show that the contour tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained, as well using the proposed RAFNN backstepping control system.     

Suppression of Late Reverberation Effect on Speech Signal Using Long-Term Multiple-step Linear Prediction

A speech signal captured by a distant microphone is generally smeared by reverberation, which severely degrades automatic speech recognition (ASR) performance. One way to solve this problem is to dereverberate the observed signal prior to ASR. In this paper, a room impulse response is assumed to consist of three parts: a direct-path response, early reflections and late reverberations. Since late reverberations are known to be a major cause of ASR performance degradation, this paper focuses on dealing with the effect of late reverberations. The proposed method first estimates the late reverberations using long-term multi-step linear prediction, and then reduces the late reverberation effect by employing spectral subtraction. The algorithm provided good dereverberation with training data corresponding to the duration of one speech utterance, in our case, less than 6 s. This paper describes the proposed framework for both single-channel and multichannel scenarios. Experimental results showed substantial improvements in ASR performance with real recordings under severe reverberant conditions.      

A Novel Audio Coding Scheme Using Warped Linear Prediction Model and the Discrete Wavelet Transform

In this paper, we present a novel audio coder using the discrete wavelet transform (DWT) and warped linear prediction (WLP). In contrast to conventional LP, WLP allows for the control of frequency resolution to closely match the response of the human auditory system. The structure of the system is similar to the transform coded excitation techniques used in wideband speech coding, where LP has been replaced with WLP, and the residual is analyzed by a wavelet filterbank designed to approximate the critical bands. The inherent shaping of the WLP synthesis filter, and a controlled bit allocation to the wavelet coefficients helps minimise the perceptually significant noise due to the quantization error in the residual. For monophonic signals sampled at 44.1 kHz, the coder achieves near transparent to transparent quality for a variety of speech and music signals at an average bitrate of about 64 kb/s. Tests also show that the coder (in its initial implementation) delivers superior quality to the MPEG layer III and comparable quality to the MPEG2-AAC codec when operating at the same bitrate.     

Regularized Linear Prediction of Speech

All-pole spectral envelope estimates based on linear prediction (LP) for speech signals often exhibit unnaturally sharp peaks, especially for high-pitch speakers. In this paper, regularization is used to penalize rapid changes in the spectral envelope, which improves the spectral envelope estimate. Based on extensive experimental evidence, we conclude that regularized linear prediction outperforms bandwidth-expanded linear prediction. The regularization approach gives lower spectral distortion on average, and fewer outliers, while maintaining a very low computational complexity.     

基于概率的自适应学习预测策略

动态电源管理技术降低系统功耗的主要办法是根据工作负载的变化动态地切换目标设备工作模式。针对自适应学习树模型的缺陷,提出基于概率的自适应学习预测策略,通过概率描述设备行为,能够提高预测正确率,从而达到系统功耗与性能之间的优化平衡。基于概率的自适应学习预测策略是一种集预测、控制、反馈为一体的预测策略。实验结果表明,该预测策略具有较好的稳定性,与其他预测策略相比可以进一步降低系统的功耗。     

一种自适应抗噪声的PPM预测模型

基于Web浏览特征提出了一种自适应抗噪声的PPM预测模型。模型在构造过程中,利用描述用户浏览深度特征的逆高斯分布及Web流行度特征,对噪声页面及过期数据进行动态移除,分别从纵向和横向上对PPM预测模型规模进行控制。实验表明,该模型对噪声数据的影响有较大的改善,能较好地动态预测用户的Web浏览特征,不仅预测准确率和存储复杂度都有一定程度的提高,而且有效控制了由预取引起的网络流量。     

Bayesian Network Models for Web Effort Prediction: A Comparative Study

OBJECTIVE – The objective of this paper is to compare, using a cross-company dataset, several Bayesian Network (BN) models for Web effort estimation. METHOD – Eight BNs were built; four automatically using Hugin and PowerSoft tools with two training sets, each with 130 Web projects from the Tukutuku database; four using a causal graph elicited by a domain expert, with parameters automatically fit using the same training sets used in the automated elicitation (hybrid models). Their accuracy was measured using two validation sets, each containing data on 65 projects, and point estimates. As a benchmark, the BN-based estimates were also compared to estimates obtained using Manual StepWise Regression (MSWR), Case-Based Reasoning (CBR), mean- and median-based effort models. RESULTS – MSWR presented significantly better predictions than any of the BN models built herein, and in addition was the only technique to provide significantly superior predictions to a Median-based effort model. CONCLUSIONS – This paper investigated data-driven and hybrid BN models using project data from the Tukutuku database. Our results suggest that the use of simpler models, such as the median effort, can outperform more complex models, such as BNs. In addition, MSWR seemed to be the only effective technique for Web effort estimation.