Statistical feature of pitch frequency distributions for robust speaker identification

This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Prequency Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead of simply taking one kind of features into account.Experimental results indicate that the hybrid approach can give outstanding improvement for text-independent speaker identification under noisy environments corrupted by AWGN.     

On using non-linear canonical correlation analysis for voice conversion based on Gaussian mixture model

Voice conversion algorithm aims to provide high level of similarity to the target voice with an acceptable level of quality.The main object of this paper was to build a nonlinear relationship between the parameters for the acoustical features of source and target speaker using Non-Linear Canonical Correlation Analysis(NLCCA) based on jointed Gaussian mixture model.Speaker indi-viduality transformation was achieved mainly by altering vocal tract characteristics represented by Line Spectral Frequencies(LSF).T...     

嵌入自联想神经网络的高斯混合模型说话人辨认

该文提出了一种嵌入自联想神经网络的高斯混合模型,它充分利用了神经网络和高斯混合模型各自的优点,以最大似然概率(ML)为准则,把它们作为一个整体来进行训练。训练过程中,高斯混合模型和神经网络的参数交替更新。由于神经网络起到了数据整形的作用,因而提高了类内数据的相似性。实验结果表明,采用该文提出的模型在各种信噪比情况下的识别率都比基线系统有所提高,最高能达到19%。     

文本无关说话人识别中一种改进的模型PCA变换方法

对于采用高斯混合模型(GMM)的与文本无关的说话人识别,出于模型参数数量和计算量的考虑 GMM的协方差矩阵通常取为对角矩阵形式,并假设观察矢量各维之间是不相关的。然而,这种假设在大多情况下是不成立的。为了使观察矢量空间适合于采用对角协方差的GMM进行拟合,通常采用对参数空间或模型空间进行解相关变换。该文提出了一种改进模型空间解相关的PCA方法,通过直接对GMM的各高斯成分的协方差进行主成分分析,使参数空间分布更符合使用对角化协方差的混合高斯分布,并通过共享PCA变换阵的方法减少参数数量和计算量。在微软语音库上的说话人识别实验表明,该方法取得了比常规的对角协方差GMM系统的最优结果有相对35%的误识率下降。     

任意信号源的盲分离

在盲信号分离中，常用的方法假设源信号的密度函数已知或由某一类带参数的函数来逼近，一旦假设的密度函数和真实的密度函数不一致或源信号不是同一分布，所用的方法就不能正确地分离出源信号。针对这一问题，本文用高斯混合模型来逼近任意分布的源信号的密度函数，并提出了一种迭代的期望最大化算法。计算机仿真表明，该算法能够有效地分离出真实的源信号。     

融入空间关系的GMM全色高分辨率遥感影像监督分割方法

为了解决高分辨率遥感影像中相同地物目标异质性和空间破碎性增大及不同地物目标的相似性增强所带来的分割新问题,该文提出一种融入空间关系的高斯混合模型(GMM)高分辨遥感影像监督分割方法。该方法首先按分割区域进行监督采样,并通过最小二乘法进行直方图拟合,对影像中的每个类别区域建立GMM用来精确表征高分辨遥感影像每个分割区域复杂的地物光谱特征;然后在GMM的概率测度域融入空间关系,使每个像素的区域所属由该像素邻域窗口内所有像素概率测度共同决定,以刻画高分辨率遥感影像中像素间的空间相关性;最后按照最大概率测度原则完成对高分辨率遥感影像的分割。为了验证文中算法的可行性与有效性分别对合成影像及真实高分辨率遥感影像进行分割实验,并和经典的FCM方法及HMRF-FCM方法进行对比,定量与定性的结果证明了文中方法能够提高分割精度。     

基于GMM聚类的空调机组多未知模态辨识方法研究

智能建筑空调机组故障检测与诊断是保证建筑环境安全、舒适、节能的基本方法.然而,多未知模态辨识仍是其中的关键难点之一.鉴于高斯混合模型(GMM)不受特定概率分布局限,可在区分类别的基础上直接得出数据的统计分布,具有优越的计算性能,且能拟合任意连续分布,本文提出一种基于GMM的空调机组多未知模态辨识方法.仿真试验结果表明,GMM聚类方法在空调机组运行模态辨识中具有较高的准确性与可靠性.     

STATISTICAL FEATURE OF PITCH FREQUENCY DISTRIBUTIONS FOR OBUST SPEAKER IDENTIFICATION

This letter proposes an effective and robust speech feature extraction method based on statistical analysis of Pitch Frequency Distributions (PFD) for speaker identification. Compared with the conventional cepstrum, PFD is relatively insensitive to Additive White Gaussian Noise (AWGN), but it does not show good performance for speaker identification, even if under clean environments. To compensate this shortcoming, PFD and conventional cepstrum are combined to make the ultimate decision, instead of simply taking one kind of features into account. Experimental results indicate that the hybrid approach can give outstanding improvement for text-independent speaker identification under noisy environments corrupted by AWGN.     

利用一种新的谱扩展技术提高窄带语音质量

提出了一种谱扩展算法,先对窄带语音进行线性预测编码(LPC)分析和多带激励(MBE)分析,得到窄带语音的线性谱频率(LSF)参数和清／浊音(U/V)判决信息;利用高斯混合模型(GMM)由窄带语音的LSF参数扩展得到高带语音的包络谱信息;对于激励信号,由窄带语音的U／V判决信息通过码本匹配得到高带的U／V判决,然后根据U／V判决信患由正弦模型合成出高带激励信号,并进而由LPC方法生成高带语音。最终的宽带语音由原始窄带语音和扩展得到的高带语音经子带合成得到.实验结果表明该算法扩展得到的宽带语音能有效提高语音信号的清晰度和可懂度。     

GAUSSIAN PRINCIPLE COMPONENTS FOR NONLOCAL MEANS IMAGE DENOISING

NonLocal Means (NLM),taking fully advantage of image redundancy,has been proved to be very effective in noise removal.However,high computational load limits its wide application.Based on Principle Comp...     

交通标志灯色饱和图像的色彩校正

针对在交通路口采集的闯红灯的彩色图像中的色饱和现象,讨论了图像中高光区域的定位、边缘色彩的确定以及色彩校正等问题。使用基于符号的GMM模型的EM算法进行最大似然估计,获得分类结果,使用区域深度快速标记算法标记深度,提出色彩校正模型,最后给出实验结果及讨论。     

Active Shape Model of Combining Pca and Ica： Application to Facial Feature Extraction

Active Shape Model （ASM） is a powerful statistical tool to extract the facial features of a face image under frontal view. It mainly relies on Principle Component Analysis （PCA） to statistically model the variability in the training set of example shapes. Independent Component Analysis （ICA） has been proven to be more efficient to extract face features than PCA. In this paper, we combine the PCA and ICA by the consecutive strategy to form a novel ASM. Firstly, an initial model, which shows the global shape variability in the training set, is generated by the PCA-based ASM. And then, the final shape model, which contains more local characters, is established by the ICA-based ASM. Experimental results verify that the accuracy of facial feature extraction is statistically significantly improved by applying the ICA modes after the PCA modes.     

基于高斯混合模型的下行小区间干扰分布

在正交频分多址接入(Orthogonal Frequency Division Multiple Access, OFDMA)蜂窝网络中,小区间干扰的统计特性与网络性能密切相关。下行小区间干扰的累积分布函数(Cumulative Distribution Function, CDF)还没有一个闭合表达式。该文提出一种参数可显式计算的高斯混合模型对下行小区间干扰分布进行近似。进一步,利用高斯混合模型将下行小区间干扰的累积分布函数近似表示为若干个误差函数的加权和。仿真验证了高斯混合模型的准确性,并且表明基于高斯混合模型的累积分布函数能很好地近似下行小区间干扰的累积分布函数。     

未知杂波条件下样本集校正的势估计概率假设密度滤波算法

在贝叶斯框架下的多目标跟踪算法中,总是假设杂波的先验信息是已知的。然而,实际应用中,杂波分布一般是未知的,假设的杂波分布往往与实际情况匹配度差,难以保证滤波精度。针对该问题,该文研究了未知杂波势估计概率假设密度(CPHD)滤波算法。首先,提出一种基于狄利克雷过程混合模型(DPMM)类的未知杂波CPHD算法,该算法能够自动选取合适的类数对杂波进行描述,有效降低了杂波空间分布估计的误差。此外,提出样本集校正的思想,并将其引入所提算法,通过去除样本集中由真实目标产生的量测,较好地解决了杂波数过估和目标数低估的问题。与传统算法相比,所提算法的滤波精度更接近于杂波信息匹配情况下的性能,仿真结果验证了其优越性与鲁棒性。