声学头模双耳录音听感效果分析

对比分析了仿真头模录音与简化声学头模录音的听感效果。选择水平面以45°为间隔的8个方向进行录音,通过听感实验判断双耳录音的方向定位正确率以及头中定位的情况。结果表明,声学头模与仿真头模在听感定位上没有显著性的差别,在双耳录音及一些其他场合可采用简化声学头模来代替仿真头模。     

双耳声学测量系统的方向特性分析（李）

简要介绍了传统的声学测量方法及其弊端,对双耳声学测量系统中的核心部分——声学头模进行了水平面、中垂面以及侧垂面的方向灵敏度测量,描述了测量方法、过程并给出了相应的数据分析.测量结果为双耳房间脉冲响应函数、双耳清晰度、双耳混响时间等双耳声学参数测量提供了空间差异性线索.     

环境色彩对语音清晰度评测的影响

在五种不同灯光色度的环境中进行汉语听感清晰度评测,并与暗室作对比。实验前后记录心境状态量表,考察环境色彩对评测结果的影响。分析表明评测结果与语音本身的信噪比有关,发现颜色环境能够引起受试心境状态的变化,从而影响评测结果。     

人工仿真头与传统立体声录音听觉空间感差异的研究

随着视听技术的发展,对声音的需求也趋于多元化。本文以实践为基础,结合传统的声学理论,对人工头仿真与传统立体声录音听感差异做一次较为细致的实验性对比研究。     

空间感的主观评测中现场评测与录音评测的比较

采用声学头模进行双耳录音评测是空间感主观评测的一种方法.利用Odeon建模仿真不同空间大小中各方向的反射声,在基准电影院中形成不同空间大小的空间感,分别使用现场评测和录音评测的方法对主观空间感进行评测,并对比两种评测方法.主观实验结果显示,对录音评测结果进行一定的线性修正,可以代替现场评测.     

声源类型对头模录音声像定位的影响

分析了声源频率以及类型对头模双耳录音声像定位的影响.采用声学头模录制了不同频率、不同时长、以及不同脉冲持续时间的声源作为实验素材,通过主观听感实验分析对声像定位的影响.结果表明,不同类型的声源在中频段定位效果最好,低频段最差.单频信号的声像定位能力要弱于频谱成分丰富的信号.当声源时长达到0.6s后,声像定位的正确率趋于稳定.实验还讨论了不同类型声源的头中效应的问题.     

基于HRTF主成分分析的头模结构分析

采用主成分分析法对不同细节结构的头模HRTF幅度谱进行降维简化处理,分析头模上的不同细节结构对HRTF的方向平均值以及谱形状基矢量相应频段的影响。结果表明,鼻子的作用可以忽略,而头发主要作用在头模后方的声源高频处。头部尺寸的影响存在于整个频段中,并且对于位于耳朵异侧的声源影响最大。对简化声学头模与仿真头模HRTF进行主成分分析结果表明,两个头模在这些特性上的差异并不十分明显,在水平面上具有很好的一致性。     

声道扩展中去相关方法的听感效果分析

通过对比分析经过子带延时法、随机相位法、MLS滤波器法、DTS Neural Mono2Stereo法以及PS22 StereoMaker法共5种去相关方法处理前后,相干声源在多声道扩展中的声场自然度、空间感、扩展性及分离度4个听感指标的听音结果发现,多声道扩展的自然度和空间感都有明显提升,其中MLS滤波器去相关法在自然度、空间感及扩展性指标方面的效果更佳。     

噪声环境中采用探询机制的局域网性能分析

轮叫探询机制已被应用于实时控制网络。本文根据发布／预定接收型通信模型，针对噪声环境，建立了该机制的令牌传送状态机，探讨了在集簇噪声影响下令牌传送的详细过程，并出轮叫探询机制的平均令牌传送时间和平均令牌循环时间，通过它们的解析表达式分析了噪声对网络性能的影响。     

噪声环境下的基音检测方法

噪声环境下的基音检测在语音信号分析和识别中占有得要地位。自相关法和平均幅度差函数是两种常用的基音检测方法。结合两种方法，提出了一种有效的噪声环境下基音检测方法。实验表明，该方法是可行的，与传统方法相比鲁棒性好，特别适用于信噪比较低的情况。     

嘈杂环境中利用CSI信息的室内轨迹跟踪技术

基于信道状态信息的室内轨迹跟踪技术近几年来备受关注。为了利用现有的商用WiFi设备实现实时的轨迹跟踪，本文采用了一发一收的室内轨迹跟踪系统，主要利用通过信道状态信息计算得到的多维参数包括飞行时间、到达角度、多普勒频移和信道衰减来计算得到相应的位置信息，并且提出了利用环境动态参数来修正定位参数的方法，以达到更为精准的轨迹跟踪效果。实验表明，采用这种方法，定位精度可以达到0.65m，证明了该方法能精确有效地实现室内轨迹的跟踪定位。      

Coding techniques for the noisy magnetic recording channel: astate-of-the-art report

Coding techniques for improving the reliability of information storage on noisy magnetic recording channels are considered. It is assumed that the Lorentzian channel model applies and that the retrieved signal is perturbed with additive white Gaussian noise. The immunity against additive noise of state-of-the-art codes such as DC-free, runlength-limited, and trellis codes are assessed     

Improved MPEG-4 still texture image coding under noisy environment

This paper describes the performance of the MPEG-4 still texture image codec in coding noisy images. As will be shown, when using the MPEG-4 still texture image codec to compress a noisy image, increasing the compression rate does not necessarily imply reducing the peak-signal-to-noise ratio (PSNR) of the decoded image. An optimal operating point having the highest PSNR can be obtained within the low bit rate region. Nevertheless, the visual quality of the decoded noisy image at this optimal operating point is greatly degraded by the so-called "cross" shape artifact. In this paper, we analyze the reason for the existence of the optimal operating point and the "cross" shape artifact when using the MPEG-4 still texture image codec to compress noisy images. We then propose an adaptive thresholding technique to remove the "cross" shape artifact of the decoded images. It requires only a slight modification to the quantization process of the traditional MPEG-4 encoder while the decoder remains unchanged. Finally, an analytical study is performed for the selection and validation of the threshold value used in the adaptive thresholding technique. It is shown that, the visual quality and PSNR of the decoded images are much improved by using the proposed technique comparing with the traditional MPEG-4 still texture image codec in coding noisy images.     

Closed-form frequency estimator based on narrow-band approximation under noisy environment

A new method for single sinusoidal frequency estimation in closed-form formula is proposed. Since sinusoidal signals are narrow-banded and white noise distribution is statistically equal in the whole spectrum, a narrow-band signal extracted from the Fourier transform of the original signal can be used to approximate the noise-corrupted sinusoidal signal. A concise closed-form formula is then deduced to estimate the frequency based on the narrow-band signal. Performance analysis and simulation results are presented, showing that the new algorithm has close performance to the Cramer-Rao bound, especially under low SNRs. It is also demonstrated that the method can be easily generalized to multi-sinusoidal signals.     

Blind separation of convolutive mixtures and an application inautomatic speech recognition in a noisy environment

We propose a two-step algorithm for the blind separation of convolutive mixtures. We discuss its application to automatic speech recognition in a noisy environment where the acoustic signals have been recorded by a microphone in a room whose furniture and walls produce echoes. The method yields good results     

A comprehensive DCF performance analysis in noisy industrial wireless networks

The use of wireless technology in industrial networks is becoming more popular because of its flexibility, reduction of cable cost, and deployment time. Providing an accurate model to study the most important parameters of these networks, the timeliness and reliability, is essential in assessing the network metrics and choosing proper protocol settings. The Institute of Electrical and Electronics Engineers (IEEE) 802.11 is a common and established wireless technology, and several analytical models have been proposed to assess its performance; however, most of them are accurate only for a limited network situation, especially data networks that have large packet payloads and are used at high signal to noise ratios, and cannot be applied to study the performance of industrial networks that have short packet lengths and are used in harsh and noisy environments. In this paper, a novel three‐dimensional discrete‐time Markov chain model has been proposed for the IEEE 802.11‐based industrial wireless networks using the distributed coordination function as the medium access control mechanism in the worst‐case saturated traffic. It considers both causes of the backoff freezing: busy channel and the successive interframe space waiting time. In this way, it provides a much more accurate estimation of the channel access and error probabilities, resulting in a more accurate network parameter calculation. Also, based on the proposed model, a comprehensive packet delay analysis, including average, jitter, and cumulative distribution function, has been provided for the near 100% reliable industrial scenario and error‐prone channel condition, which in comparison with similar pieces of work provides much more accurate results. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimum spatial processing in a noisy environment for arbitrary antenna arrays subject to random errors

A procedure is presented for the maximization of the expected signal-to-noise ratio improvement factor for arbitrary antenna arrays whose excitation amplitudes and phases, as well as element positions, are subject to random errors. In its general form, the formulation imposes no restrictions on either the probability distribution or the variance of the random errors. Correlations are allowed to exist between the random variations in array parameters, and the effect of system internal noise is considered. Computed results for a linear endfire array in a typical noise environment are given, illustrating the dependence of the signal-to-noise ratio improvement factor on the system internal noise; the system bandwidth; the amplitude, phase, and position errors; and the error correlation intervals. Typical expected power pattern functions are also plotted.     

噪声环境下基于单像素成像系统和深度学习的目标识别方法

单像素成像系统由于其独特的成像方式受到广泛关注,但其在噪声环境中的目标识别方法并未得到深入研究。针对该问题,文中分别采用桶探测器获取的信号值和重构出的二维图像作为训练样本进行深度学习,并以此识别噪声环境中的目标。通过对比两者识别结果,发现在采样率较低时,前者即使在较强噪声环境中也可以获得较高的识别率;而后者的识别率虽然一直比较稳定,但其预处理时间较高,因此前者更适用于快速成像中的目标识别。此外,对于仅利用桶探测器信号进行训练的方法,文中还研究了目标稀疏度对其识别精度的影响,发现当外界噪声和采样率一定时,稀疏度越高的目标,其识别精度也越高。文中为噪声环境中单像素成像的目标识别方法提供了选择依据。