面向虚拟战场的实时立体声合成

文中面向虚拟战场给出一套利用多媒体计算机实现实时立体声合成的简化算法。     

何为第三代物理5.1声道音效耳机 硕美科多声道游戏耳机探秘之旅

在它问世之前，大家只能使用双喇叭模拟立体声或者环绕音效的耳机（俗称：虚拟多声道耳机），而要想听到真实的多声道环绕音效，我们只能购买昂贵的组合音响或者奔赴电影院才可实现。然而随着硕美科第三代物理5．1声道音效技术的出现，让我们用耳机听真实多声道声音的梦想成为可能。     

个人电脑环绕立体声系统的选购

随着SB Live！、MX300等一系列高性能PCI声卡的问世,支持多声道扬声器输出已成为个人电脑音频系统的全新标志。由于EAX、A3D等全新三维音效标准日渐走红,带动了高档PCI声卡和多点式音响系统的销售。面对多声道音频系统的诱惑,我们应该如何正确的选择和搭配,是笔者在这里要与大家交流的内容。 选择多声道环绕音频系统的理由:选择多声道环绕音频系统的理由是因为普通的16位立体声声卡不能表现出声场中环绕声道     

AVS-P10立体声编码算法的优化设计与实现

AVS-P10是我国首部具有完全自主知识产权的移动音频编解码标准,但是其立体声编码存在重建声像不够稳定、编码复杂度较高的问题。根据参数立体声编码原理,采用声道下混和立体声参数提取及合成技术,以AVS-P10的核心编码器为基础,设计并实现了一种高效的立体声编解码方案。实验结果表明,同等码率下,优化后的算法比AVS-P10立体声编码算法的主观音质提升约10MUSHRA得分,编码复杂度下降幅度达到40%~60%,解码复杂度略有下降。     

耳机也能5.1 硕美科ST-1603试听

众所周知,要实现5.1环绕立体声效果需要两个方面的配合:一是合理摆位的多声道音箱,二是支持多声道输出的音源。ST-1603采用USB接口,在线控模块集成声卡,解决了音源的问题。而在ST-1603硕大的耳机罩下,分布着六个发声单元,声音正是通过声卡     

杜比(Dolby)造天籁

所谓环绕声是指在声场中位于听者后方的声场,这部分声场主要是巾混响声构成的,因而其特点是无固定方向,均匀地向各个方向传播。因其包围着或者说环绕着听音者,使听音者获得中间感和临场感,故而得名。 通常说的环绕声实际上是指“环绕立体声”,即在双声道立体声和多声道立体声基础上发展起来的     

产业信息

为蓝牙耳机参考设计提供完整平台实现Hi-Fi立体声播放和优质语音呼叫皇家飞利浦电子公司日前推出易于使用的新型蓝牙1.2立体声耳机参考设计。这一蓝牙立体声解决方案能在单个器件中同时支持立体声和语音。现在,用户可以通过手机或便携式媒体播放器欣赏音乐,而不会面临漏接重要电话的风险。它能提供优质的无线语音和音频体验,还支持无线耳机、手机及家中和车内无线扬声器等诸多应用。该蓝牙1.2立体声参考设计(OM6777),能为音频流应用提供基于立体声编码解码器的即插即用解决方案。新产品是为耳机/耳麦设计的,采用了优化的语音算法以改进通…     

I~2C总线控制的声效DSP集成电路——SAA7712H

SAA7712H是飞利浦半导体公司推出的用I~2C总线控制的声效DSP处理集成块。它被广泛用于电视和家庭影院系统,提供Dolby prologic、Incredible和3D环绕声及其它模式的声效处理。与协处理器的多声道解码(AC-3和MPEG2)配合使用,可用作后期声效处理。 SAA7712H适用于数字信号解码后的声效处理。特设的声效模式可使非立体声输入信号产生模拟立体声效果。其立体声输入信号产生模拟立体声效果。其“立体声扩展”特别适合于电视机和音箱间距离受限制     

在TMS320C6x系列DSP上实现G．729算法

在研究ITU-T的G．729CS-ACELP语音编码算法基础上，本文介绍了使用TI公司的TMS320C6x系列DSP实现该算法的一些问题以及一些应用中的具体优化技术，这在一定程度上降低了该算法的计算复杂度，而输出语音仍然保持了很高的合成品质。然后，本文讨论了如何在一个电信级 的应用内核上对该语音编码算法进行多声道扩展的问题，该多通道编码系统基本能达到实用要求。最后，在实时MCPS、内存要求、处理延时和实时性能等方面对该DSP实现的编码器做了一个评价，可以作为其他语音编码器开发的参考。     

高清三分天下——不同存储介质高清数码摄像机专题

随着符合高清标准的平板电视、显示器和播放器及游戏设备等产品的出现，有越来越多的人开始希望能体验到高清影像带来的全新感受，从清晰锐利、艳丽明亮的影像画面到震撼动人的多声道立体声环绕系统，高清标准完全让影音的表现进入到了一个全新的时代。     

改进的跨尺度引导滤波立体匹配算法

作为双目三维重建中的关键步骤,双目立体匹配算法完成了从平面视觉到立体视觉的转化.但如何平衡双目立体匹配算法的运行速度和精度仍然是一个棘手的问题.本文针对现有的局部立体匹配算法在弱纹理、深度不连续等特定区域匹配精度低的问题,并同时考虑到算法实时性,提出了一种改进的跨多尺度引导滤波的立体匹配算法.首先融合AD和Census变换两种代价计算方法,然后采用基于跨尺度的引导滤波进行代价聚合,在进行视差计算时通过制定一个判断准则判断图像中每一个像素点的最小聚合代价对应的视差值是否可靠,当判断对应的视差值不可靠时,对像素点构建基于梯度相似性的自适应窗口,并基于自适应窗口修正该像素点对应的视差值.最后通过视差精化得到最终的视差图.在Middlebury测试平台上对标准立体图像对的实验结果表明,与传统基于引导滤波器的立体匹配算法相比具有更高的精度.     

A fast stereo matching algorithm suitable for embedded real-time systems

In this paper, the challenge of fast stereo matching for embedded systems is tackled. Limited resources, e.g. memory and processing power, and most importantly real-time capability on embedded systems for robotic applications, do not permit the use of most sophisticated stereo matching approaches. The strengths and weaknesses of different matching approaches have been analyzed and a well-suited solution has been found in a Census-based stereo matching algorithm. The novelty of the algorithm used is the explicit adaption and optimization of the well-known Census transform in respect to embedded real-time systems in software. The most important change in comparison with the classic Census transform is the usage of a sparse Census mask which halves the processing time with nearly unchanged matching quality. This is due the fact that large sparse Census masks perform better than small dense masks with the same processing effort. The evidence of this assumption is given by the results of experiments with different mask sizes. Another contribution of this work is the presentation of a complete stereo matching system with its correlation-based core algorithm, the detailed analysis and evaluation of the results, and the optimized high speed realization on different embedded and PC platforms. The algorithm handles difficult areas for stereo matching, such as areas with low texture, very well in comparison to state-of-the-art real-time methods. It can successfully eliminate false positives to provide reliable 3D data. The system is robust, easy to parameterize and offers high flexibility. It also achieves high performance on several, including resource-limited, systems without losing the good quality of stereo matching. A detailed performance analysis of the algorithm is given for optimized reference implementations on various commercial of the shelf (COTS) platforms, e.g. a PC, a DSP and a GPU, reaching a frame rate of up to 75 fps for 640 × 480 images and 50 disparities. The matching quality and processing time is compared to other algorithms on the Middlebury stereo evaluation website reaching a middle quality and top performance rank. Additional evaluation is done by comparing the results with a very fast and well-known sum of absolute differences algorithm using several Middlebury datasets and real-world scenarios.     

基于区域匹配的实时加速技术

针对区域立体匹配计算量大实时性差的困难,分析了相关匹配算法的实际工作过程,采用消除冗余因子和Box滤波、多级分辨率匹配减小计算复杂度,对算法结构进行了改进和优化,并利用超线程和OpenMP技术对算法进行了加速,提出了一种实时区域匹配算法.对算法进行实验,结果表明算法符合了视觉导航的准确性和实时性要求,并且对于提高其他区域匹配算法实时性也具有重要借鉴意义.     

Real-time computation of disparity for hand-pair gesture recognition using a stereo webcam

This paper presents an algorithm for the real-time computation of disparity using video stereo images captured by a stereo webcam. This algorithm is designed to provide both real-time throughput and robust disparity estimation for real-world applications where computation is limited to a pre-defined region-of-interest (ROI). More specifically, this algorithm is used as part of a hand-pair gesture recognition application where the disparity is computed for two ROI around a hand-pair identified by the segmentation component of the recognition application. The developed algorithm provides the required relative difference in disparity with background at high frame rates for the hand-pair gesture recognition application. The results obtained with an inexpensive commercial VGA stereo webcam show a robust disparity computation of 20?ms/frame enabling real-time hand-pair gesture recognition at 25?fps with >90% recognition rate for a maximum hand speed of 40?cm/s and for hand distances between 30 and 150?cm away from the camera.     

Real-Time Correlation-Based Stereo Vision with Reduced Border Errors

This paper describes a real-time stereo vision system that is required to support high-level object based tasks in a tele-operated environment. Stereo vision is computationally expensive, due to having to find corresponding pixels. Correlation is a fast, standard way to solve the correspondence problem. This paper analyses the behaviour of correlation based stereo to find ways to improve its quality while maintaining its real-time suitability. Three methods are suggested. Two of them aim to improve the disparity image especially at depth discontinuities, while one targets the identification of possible errors in general. Results are given on real stereo images with ground truth. A comparison with five standard correlation methods is provided. All proposed algorithms are described in detail and performance issues and optimisation are discussed. Finally, performance results of individual parts of the stereo algorithm are shown, including rectification, filtering andcorrelation using all proposed methods. The implemented system shows that errors of simple stereo correlation, especially in object border regions, can be reduced in real-time using non-specialised computer hardware.     

基于背景感知相关滤波的深度提取跟踪算法研究

背景感知相关滤波算法将目标背景和前景一起建模[1],利用包含背景信息的负样本进行训练,得到高鲁棒性核相关滤波器,实现跟踪目标与背景的分离。这种单目实现的相关滤波跟踪算法能够做到实时跟踪,却以丢失深度为代价。本文提出了一种将双目立体视觉深度提取与基于单目的背景感知相关滤波相结合的算法,该算法在做到实时跟踪的同时,能够对视频序列中的若干帧利用改进的双目立体匹配SGM算法得到视差图,反馈出跟踪目标的深度信息。实验结果表明该算法具备实时性,且三维坐标定位准确。     

改进Harris-SIFT算法在双目立体视觉中的应用

针对双目立体视觉图像匹配的实时性问题,提出了一种改进Harris-SIFT算法,克服了原SIFT算法提取的特征点不是角点且耗时长等问题。该算法首先用改进Harris算子进行角点提取,然后用SIFT算子构建特征描述子,最后对提取的特征点采用欧氏距离度量点对的相似性,利用最近邻搜索策略进行特征匹配。在VC++6.0与Open CV平台上,通过实验比较了所提算法与SIFT算法的特征点提取匹配结果,证明了所提算法的有效性与实时性。