A Weighted Feature Voting Approach for Robust and Real‐Time Voice Activity Detection

This paper concerns a robust real‐time voice activity detection (VAD) approach which is easy to understand and implement. The proposed approach employs several short‐term speech/nonspeech discriminating features in a voting paradigm to achieve a reliable performance in different environments. This paper mainly focuses on the performance improvement of a recently proposed approach which uses spectral peak valley difference (SPVD) as a feature for silence detection. The main issue of this paper is to apply a set of features with SPVD to improve the VAD robustness. The proposed approach uses a weighted voting scheme in order to take the discriminative power of the employed feature set into account. The experiments show that the proposed approach is more robust than the baseline approach from different points of view, including channel distortion and threshold selection. The proposed approach is also compared with some other VAD techniques for better confirmation of its achievements. Using the proposed weighted voting approach, the average VAD performance is increased to 89.29% for 5 different noise types and 8 SNR levels. The resulting performance is 13.79% higher than the approach based only on SPVD and even 2.25% higher than the not‐weighted voting scheme.     

基于场景相似性度量的SAR图像抑斑

通过研究SAR图像的信号相关加性噪声模型,结合提出的加性残留噪声模型得到一种图像域的期望加权滤波器,并引入像素间的相似性作为加权时的权值。接着,指出传统像素间相似性的度量方法不能鲁棒地用于噪声图像,提出从图像的灰度特性和空间结构两个方面考虑,利用灰度和梯度距离度量像素间的相似性,并应用局部场景之间的相似性估计像素间的相似性使之可靠地应用于SAR图像。实验表明,所提出的算法具有相当出色的抑斑性能,在有效的抑制斑点噪声的同时,均匀区域几乎没有出现“振铃”效应,并且非常好地保持了原始图像的边缘、纹理和点状目标。     

基于分类线性加权的源-目标话者声音转换算法的研究

源-目标话者的声音转换是一种变换说话人声音特性的技术,它将源说话人的声音转换成另一个指定的目标说话人的声音.对源话者声道谱特性的修改是声音转换的关键之一.为了克服一般分类线性转换算法中分类不准确所带来的误差,本文引入了分类线性加权转换的策略,根据不同子类的转换函数对谱特性的贡献,赋予不同的加权系数,给出了一种基于GMM后验概率加权的线性转换算法.在微软汉语普通话语音数据库上做的四组对比实验表明,该算法在谱转换性能上均有不同程度的提高.     

视频解码器中插值与加权预测的硬件实现

设计了支持H.264/JVT/AVC标准和AVS标准的插值与加权预测的硬件结构。整个设计在其所属的视频解码器中是以宏块为单位处理的,内部则以可变块为单位处理。为了提高插值的速度,双向预测并行处理,在插值模块的内部则做6级流水(H.264)或8级流水(AVS)。加权预测同样也做了4级流水。整个设计在Modelsim下的仿真结果正确,用XST在VIRTEXⅡ4000,-6上综合频率为98 MHz,所用SLICE约为10 000,预期整个解码器设计能支持1080i@30fps的高清实时解码。     

Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.     

改进遗传算法在DOA搜索中的应用

在对短时、低信噪比信号测向时。加权子空间测向算法（WSF）的统计性能优于Music,Esprit等测向算法,且可以对相干信号进行检测。但是因为算法实现涉及到非线性多维搜索。因此运算量很大。本文提出的改进遗传算法,是一种运算速度较快的加权子空间算法的实现算法。该算法将免疫算法引入遗传算法中,并与MVP算法相结合。解决了普通遗传算法（CGA）在DOA搜索中易早熟。后期搜索效率低的问题,拥有快速、全局的搜索能力。计算机仿真证明了该算法的有效性和鲁棒性。     

多传感器数据融合提高水声定位精度

水下水声环境具有复杂性和不确定性,造成单个传感器接收信息不全和不可靠,定位水下目标精度不高,采用多传感器联合定位成为一个趋势。针对采用多传感器数据融合提高水声定位精度,提出了两种数据融合的方法,优化加权平均法和自适应加权平均法,当待融合数据为两个时,通过理论分析得出了计算加权因子的公式。由数值仿真得到了两种方法的融合精度,结果表明优化加权平均法和自适应加权平均法都可以有效地改善定位精度,但是后者的效果明显优于前者。     

SAR图像相干斑滤波性能评估模型

提出针对单极化合成孔径雷达(SAR)图像相干斑滤波算法性能的分层检验模型和综合评价方法.模型分相干斑抑制程度和目标微波后向散射系数保持程度两个层次,包含的指标有等效视数,信号杂渡比、回波辐射度损失、均值偏差、空间分辨率损失和峰值旁瓣比偏差六项.综合评价运用了多指标决策(MCDM)理论,通过线性分级的模糊语义标度,运用有序加权平均(Ordered Weighted Averaging,OWA)算子对分层指标检验值构成的性能偏好矩阵进行集结,得到相干斑滤波器的性能综合评价结果.通过真实和模拟的SAR图像数据,有效地对增强Lee、Gamma MAP、EPOS、小波软阈值和Kuan滤波算法进行了性能检验和评估,得到与主观综合评价一致的结果.     

基于遗传算法的导航实时图像匹配算法

由于一般图像匹配算法均采用全局搜索法,耗时较大,为满足景象匹配辅助导航系统实时性的要求,提出了一种将遗传算法和加权Hausdorff距离算法相结合的图像匹配算法,利用遗传算法的非遍历搜索机制,迅速收敛到全局近似最优解,提高了匹配搜索的快速性.同时,提出了一种基于特征图像分支点提取的加权Hansdorff距离图像匹配算法,并给出了相应的权值求解公式,利用加权Hausdorff距离作为遗传算法的适应度函数,能够明显减少匹配搜索的计算量,提高匹配结果的精度.仿真分析表明,将遗传算法和加权Hausdorff距离算法相结合的图像匹配算法能够很好地满足景象匹配辅助导航系统的实时性和精度要求.     

人工神经网络在轻板隔墙隔声预计中的应用

轻钢龙骨薄板墙(简称轻板隔墙)的隔声性能由于其构造的复杂性,没有简单公式可以预计其隔声量。在大量实验结果的基础上,利用人工神经网络对一些常见的轻板墙组合构造进行计权隔声量(RW)的预计。根据相关分析和主成份分析的结果,选用板材面密度、墙体总厚度、腔内吸声层厚度、龙骨型式和垫层等因子作为网络的输入变量,由此进行隔声性能的预计,可达到较满意的结果。