音乐·个性·心境——介绍刘星新作《孤独神》

时下,音乐歌曲的商业味越来越浓。每天只要打开收音机、电视机,所听所看的都是商品音乐,不管你喜不喜欢,都要面对这些“精心包装”的音乐歌曲,无法选择。几个集团泡制出的几大天王、歌后,让少男少女们塑造出他们心目中的偶像,从而找到献金者,偶像崇拜者就是其挖掘的金矿。宣传机构也大肆渲染,不论内容如何,反正有人播就有人听。听众也往往是盲从的,受羊群心理所摆布,流行歌曲也就这样一首接一首地“推陈出新”起来。不论你有多大的才华,多大的抱负,不依商业社会的规则就不能打入圈子。但尽管如此,还是有不少爱乐人对流行歌曲不屑一听;有     

“亚乐迷”的品味

我承认自己是一个对古典音乐大部头缺乏耐心的人,所以从发烧友或专业的角度看来,也不是一个真正的音乐爱好者。但是生活中又不可能没有音乐,流行歌曲除外,这一点相信不少人亦有同感。于是,随着一段段时间的心境变化,便爱上了诸如《神秘园》、恩雅一类的音乐,所谓的新音乐。这也许是在当下滚滚红尘的浮躁中,最为偷懒又最为轻松的接近和倾听音乐的办法。     

中国古典民乐靓碟欣赏(一)

当前的音乐可谓百舸争流。流行歌曲、交响曲及各种轻音乐、现代音乐等百家争鸣、万花齐放。中国民乐是世界上最古老、高雅的音乐,代表着我们祖国几千年悠久的历史和灿烂的文化,包含着世代艺人的心血和才华。如今,这一切似乎正在为人们所遗忘！感慨之余,特在此向广大同行(尤其是年轻的同行)推荐一些中国古典民乐的精华。     

基于内容的音乐节拍跟踪

 节奏是音乐的三大要素之一,对其准确的分析和提取具有重要的研究意义.节奏特征主要分为音乐节拍和速度.本文首先提出了一种利用自相关相位-熵序列分析音乐节拍结构及音乐速度的方法.利用该方法对50首流行歌曲及50首纯乐器音乐速度的分析结果可达到97%;在速度分析结果基础上,文中还给出了节拍点求解过程的近似贝叶斯模型,使得节拍点序列在整体上与音乐信号的长时速度保持一致;文中在最后给出了利用动态规划思想进行音乐节拍跟踪的新方法,完成了音乐节拍跟踪实验,并通过与其它实验的结果比较,验证了算法的有效性.     

美国流行音乐史话(一)

最旱的流行音乐 殖民时期的美国,清教主义对音乐持反对态度,只赞同其为宗教服务的功能,从而使大量的赞美诗、圣歌问世,粗识五音者皆唱,每有集会必歌;追根溯源,这算得上是最早的美国“流行音乐”了。到了19世纪,世俗流行歌曲大为勃兴,涌现出相当数量的流行歌曲作曲家,其中特别值得一提的是斯     

基于预分割的说话人分割方法

对传统的基于贝叶斯信息准则(BIC)的说话人分割方法进行了改进,通过引入预分割环节来降低说话人分割的计算量.理论分析和实验验证表明,基于预分割的说话人分割方法在基本保证分割性能的前提下,运算量得到了有效控制.     

陈年佳酿味更醇 记一套中西合璧的靓声组合

这是一套有着比较鲜明特色的双声道音响系统。通常情况下发烧友们都很注重系统的平衡性,希望在整个频段内都有均衡的量感,因为这样可以获得对唱片录音的最忠实还原。不过事实上很多人在潜意识中仍然在对个性进行着追求,并且在不知不觉中体现出来。例如喜欢听弦乐人声的,必定会注重系统的中频表现力;听现代音乐和流行歌曲时低音就很重要;     

夜风如熙 秋思如绪

费星最先学的是美术。学完美术后，还是感觉自己在音乐方面更有天赋。后来投考江汉大学艺术系，在这所学校里打下较坚实的专业基础。毕业后她分在武汉歌舞剧院工作，唱起了流行歌曲。费星有很长一段时间在酒吧唱歌，甚至可以说，她是在酒吧中成长起来的歌手。听过她演唱的人是这样评价的：嗓音醇厚，庸怠而不失质朴，率真却透射着华贵，音乐的细节把握准确。     

复杂背景的快速人脸检测研究

对于彩色图片,为了能够快速地在复杂背景中检测出多姿态人脸,解决肤色分割后大量粘连的类肤色和肤色区域等问题,在阐述光照和色彩补偿、肤色分割等算法的同时,提出了利用投影法和GLV法相结合的改进算法及利用人的头发来定位人脸的方法.实验结果表明,该方法能够在很短的时间内较为准确地检测出复杂环境中旋转不超过45°的多姿态人脸.因...     

电脑音箱也发烧

在一般人的心目中,电脑音箱不就是一两百块钱的所谓2.1多媒体音箱吗？音质虽然不咋的,但看看电影、听听流行歌曲还是挺方便。其实,对于音乐爱好者而言,电脑音箱应该像用来欣赏音乐的高档音箱那样,也是HiFi发烧级的。笔者下面为大家介绍的几款产品就是发烧级电脑音箱的代表。     

基于多目标Camshift手势识别

基于单目视觉下的手势识别技术一般由手势建模、特征提取、手势匹配等几个关键技术构成。手势跟踪算法目前主流的是粒子滤波算法和Camshift算法。系统采用Camshift算法,将人手图像由RGB空间转换到HSV空间后,在HSV空间利用半自动预定义模板颜色对人手进行分割,并对其进行改进实现多目标跟踪,由于Camshift算法为半自动算法,在对手势进行跟踪前需对手势进行手动标定,系统采用了手势跟踪与手势识别技术结合的方法,改进了Camshift算法,解决了Camshift的半自动问题和实现多目标跟踪,实现双手的手势识别。     

基于Boot Strapping的中文实体关系自动生成

针对中文信息抽取系统中建立提取事件模板的难点问题，基于Bootstrapping思想。提出一种简单、可行的实体关系自动生成方法，利用由种子词和种子模板组成的知识库建立学习器，采用标量聚类的方法，通过种子模板抽取更多的与种子词相似语义关系的特征词。在此基础上，利用最近邻居的原则。进而生成更多的抽取模板。丰富了知识库，为分析二元实体关系奠定基础，使得生成复杂的消息模板成为可能，同时极大地减轻手工建立模板的复杂度．有利于系统进行移植。     

ECG analysis using nonlinear PCA neural networks for ischemiadetection

The detection of ischemic cardiac beats from a patient's electrocardiogram (EGG) signal is based on the characteristics of a specific part of the beat called the ST segment. The correct classification of the beats relies heavily on the efficient and accurate extraction of the ST segment features. An algorithm is developed for this feature extraction based on nonlinear principal component analysis (NLPCA). NLPCA is a method for nonlinear feature extraction that is usually implemented by a multilayer neural network. It has been observed to have better performance, compared with linear principal component analysis (PCA), in complex problems where the relationships between the variables are not linear. In this paper, the NLPCA techniques are used to classify each segment into one of two classes: normal and abnormal (ST+, ST-, or artifact). During the algorithm training phase, only normal patterns are used, and for classification purposes, we use only two nonlinear features for each ST segment. The distribution of these features is modeled using a radial basis function network (RBFN). Test results using the European ST-T database show that using only two nonlinear components and a training set of 1000 normal samples from each file produce a correct classification rate of approximately 80% for the normal beats and higher than 90% for the ischemic beats     

Transaction-Centric Reconciliation in Disconnected Client–Server Databases

As mobile computing devices become more and more common, mobile databases are becoming popular. An important feature of these database systems is their ability to allow optimistic replication of data by providing disconnected mobile devices the ability to perform local updates. The key problem to this approach is the reconciliation problem, i.e. the problem of serializing potentially conflicting updates from disconnected clients on all replicas of the database. Reconciliation of conflicting updates is especially critical for disconnected databases where disconnected updates are allowed. We examine some choices for providing solutions to the reconciliation problem. We then present an algorithm that combines multiversion concurrency control schemes on a server with reconciliation of updates from disconnected clients. We also describe a simple but illustrative sample application. Finally we present our conclusions.     

Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring

A new detector is presented which finds changes in the repolarization phase (ST-T complex) of the cardiac cycle. It operates by applying a detection algorithm to the filtered root mean square (rms) series of differences between the beat segment (ST segment or ST-T complex) and an average pattern segment. The detector has been validated using the European ST-T database, which contains ST-T complex episodes manually annotated by cardiologists, resulting in sensitivity/positive predictivity of 85/86%, and 85/76%, for ST segment deviations and ST-T complex changes, respectively. The proposed detector has a performance similar to those which have a more complicated structure. The detector has the advantage of finding both ST segment deviations and entire ST-T complex changes thereby providing a wider characterization of the potential ischemic events. A post-processing stage, based on a cross-correlation analysis for the episodes in the rms series, is presented. With this stage subclinical events with repetitive pattern were found in around 20% of the recordings and improved the performance to 90/85%, and 89/76%, for ST segment and ST-T complex changes, respectively.     

An enhance excavation equipments classification algorithm based on acoustic spectrum dynamic feature

Underground pipeline network surveillance system attracts increasingly attentions recently due to severe breakages caused by external excavation equipments in the mainland of China. In this paper, we study excavation equipments classification algorithm based on acoustic signal processing and machine learning algorithms. A cross-layer microphone array with four elements is designed to collect the acoustic database of representative excavation equipments on real construction sites. The generalized sidelobe canceller algorithm is employed for background noise reduction. The improved spectrum dynamic feature extraction algorithm is then implemented for the benchmark acoustic feature database construction of excavation equipments. To perform classification and background noise identification, the single hidden layer feedforward neural network is employed as the classifier. An improved algorithm based on the popular extreme learning machine (ELM) is proposed for classifier learning. The leave-one-out cross validation strategy is adopted for the regularization parameter optimization in ELM. Comprehensive experiments are conducted to test the effectiveness of the proposed algorithm. Comparisons with state-of-art classifiers and the Mel-frequency cepstrual coefficients acoustic features are also provided to demonstrate the superiority of our approach.     

基于密度聚类的网络性能故障大数据分析方法

针对层出不穷的网络安全事件,如何快速在海量监测数据中发现异常数据,并开展网络故障分析成为研究难点。针对该问题,提出一种基于密度聚类的网络性能故障大数据分析方法,通过熵权分析、数据清洗与标准化处理实现关键性能特征提取与数据整形,基于参数调优的DBSCAN聚类算法提取性能故障异常数据。基于实时采集的全国多家运营商海量骨干网链路性能数据验证该算法,结果表明,与人工标注网络性能异常数据相比,其识别的准确性超过90%,可满足开展全国网络运行故障分析的需求。     

Key-frame-based depth propagation for semi-automatic stereoscopic video conversion

In this paper, we propose a key-frame-based bi-directional depth propagation algorithm for semi-automatic 2D-to-3D stereoscopic video conversion. First, key-frames are identified from each video shot based on color motion-compensation errors to prevent high-motion content between any pair of consecutive key frames. Depths for key-frames are manually assigned or rendered by popular computer tools, and then bi-directionally propagated to non-key-frames there between. Our depth propagation algorithm is featured of a multi-pass error correcting procedure for each frame to prevent depth artifacts from being further propagated to adjacent frames. Our proposed algorithm is advantageous in solving the background occlusion/dis-occlusion problem that degrades the performances of traditional depth propagation algorithms. Experimental results show that our scheme is capable of achieving better results against three prior algorithms in view of the qualities of the estimated depth map (e.g., dis-occluded background and object boundaries) and the synthesized stereo views.     

多层融合深度局部PCA子空间稀疏优化特征提取模型

子空间方法是主要利用全局信息的经典模式识别方法,随着深度学习思想的引入,局部自学习结构特征模型得到大家的关注.利用深度学习原理,本文提出一种多层融合的深度局部子空间稀疏优化特征自学习抽取模型解决目标识别问题.首先,对训练样本集通过最小化重构误差得到第一层的主成分（Principal Component Analysis,PCA）特征映射矩阵;然后,通过L₁范数约束对特征映射结果进行稀疏优化,提高算法鲁棒性.接着,在第二层映射层以第一层的特征输出为输入,进行同样的特征矩阵学习操作,最终将图像映射至深层PCA子空间;然后,对各个映射层的特征提取结果进行加权融合,进行二值化哈希编码和直方图分块编码,提取图像的深度子空间稀疏特征.在FERET、AR、Yale等经典人脸数据库以及MNIST、CIFAR-10等目标数据库上的实验结果表明,该算法可以取得较高的识别率以及较好的光照、表情、人脸朝向鲁棒性,并且相对于卷积神经网络等深度学习框架具有结构简洁、收敛速度快等优点.     

Oracle 11g网页注入中数据返回攻防研究

SQL注入攻击是近几年来非常流行的针对数据库的攻击手段,因为经由Web页面可以绕过防火墙来渗透攻击数据库系统,进而攻击操作系统,所以具有强大的破坏性。在通过Web页面实施SQL注入的过程中,如何将查询结果返回给攻击者（即数据返回技术）是一个非常核心的技术内容。研究对象是针对Oracle 11g为后台数据库的网站,对其实施SQL注入攻击中的5种数据返回技术,以及与之相对应的各种防御手段。