FD Massive MIMO协作多点传输场景下提升系统频谱效率的算法研究

近年来,随着智能终端数量的增长与第五代移动通信技术的发展,移动通信系统对系统频谱效率以及数据吞吐量的需求越来越高。因此,如何能提升未来无线移动通信系统频谱效率成为了5G研究的重点方向。在第五代无线移动通信新空口技术(5G NR)研究进程中,协作多点传输技术(CoMP)通过其多个传输点的联合传输,降低小区间干扰(Inter-cell Interference),从而提升小区边缘用户的覆盖性能,实现了网络容量以及频谱效率的大幅提升。另一方面,全维度大规模多输入多输出技术(FD Massive MIMO)充分运用了FD MIMO的维度资源、波束赋形以及角度调整技术以及Massive MIMO的大规模的天线跟波束资源,进一步实现了网络频谱效率的提升。本文在FDMassiveMIMO协作多点传输场景下深入研究了提升系统频谱效率的算法,通过对比分析总结出一种有效提升频谱效率并同时实现干扰协调的算法。最后,给出了系统级仿真结果并证明了该算法在提升系统频谱效率方面的优越性能。     

基于PSOLA与DCT的情感语音合成方法

情感语音合成可以增强语音的表现力,为使合成的情感语音更自然,提出一种结合时域基音同步叠加(PSOLA)和离散余弦变换(DCT)的情感语音合成方法。根据情感语音数据库中的高兴、悲伤、中性语音进行韵律参数分析归纳情感规则,调整中性语音各音节的基音频率、能量和时长。使用DCT方法对基音标记过的语音段进行基音频率的调整,并利用PSOLA算法修改基音频率使其逼近目标情感语音的基频。实验结果表明,该方法比单独使用PSOLA算法合成的情感语音更具情感色彩,其主观情感的识别率更高,合成的情感语音质量更好。     

基于傅立叶变换的语音频谱平滑算法

传统的基于源-滤波器模型的语音频谱平滑算法,需要将语音信号分解为激励源和对应的声道滤波器,这样做会引入误差,最终导致合成语音质量的下降。研究了一种基于傅立叶变换的语音频谱平滑算法,避免了上述的分解步骤。实验表明,这种方法能够较好地进行语音频谱平滑并且使合成语音的质量没有受到太大影响。     

无源毫米波成像最大似然频域校正超分辨算法

在无源毫米波成像中, 因为天线孔径大小的限制而导致获取的图像分辨率低, 所以必须采取有效的后处理措施增强分辨率. 本文提出了一种针对无源毫米波成像应用的最大似然频域校正超分辨算法. 该算法首先使用Wiener滤波复原算法恢复图像通带内的频谱分量, 然后运用Richardson-Lucy算法实现频谱外推, 最后通过一种频域校正算法, 用Wiener滤波器恢复的频谱代替通带内的频谱, 保证图像的低频分量不被破坏. 实验结果表明, 该算法改善了收敛速度, 增强了图像的分辨率, 同时能够有效地减轻恢复图像中的振铃波纹, 有利于无源毫米波成像超分辨的实现.     

相控阵雷达与频谱无人机监测平台的数据处理与融合

为了提升安防系统对于无人机的侦测能力,以相控阵雷达、频谱无人机侦测系统为硬件基础,提出了雷达去噪算法以及多源异构数据融合算法来提升无人机侦测系统的有效性及准确性。其中,提出了基于批号库更新策略的雷达去噪算法,该算法解决了相控阵雷达系统的侦测结果存在噪声点的问题,使得侦测结果更清楚、准确。针对雷达和频谱侦测目标坐标存在误差,将雷达系统侦测到的距离信息与频谱系统侦测到的角度信息相结合,提出了多源异构数据融合算法,解决了相控阵雷达和频谱系统数据融合的问题。通过实地无人机的放飞实验,证明了所提出方法的有效性。     

基于网格频谱域的二维图形水印算法

提出了基于网格频谱域的二维矢量图形改进水印算法,以置乱后的二值图象作为水印,以二维矢量图形作为载体。通过修改载体图形随机挑选的网格频谱域系数来嵌入水印。水印嵌入强度能够根据图形的自身曲线弯曲变化频度特征自适应地调整。实验结果表明,相对于原算法。改进算法具有更好的安全性,且在保证水印具有较好透明性的前提下。提高了水印的健壮性。     

用于隐马尔可夫模型语音带宽扩展的激励分段扩展方法

语音带宽扩展通过人为恢复窄带语音的频谱带宽来提高语音听觉质量。针对源滤波器扩展模型的激励扩展问题,提出一种分段扩展方法。该方法在扩展带的低频段与高频段部分分别采用窄带激励源的高频部分与帧能量等效的白噪声作为激励信号,最后两者与原窄带激励组成宽带激励信号。基于隐马尔可夫模型（HMM）谱包络估计的宽带语音重构实验结果表明：该方法降低了重建语音的失真度,恢复重建的语音信号优于谱平移激励扩展方法。     

基于FDTD的三维全波电磁分析软件及工程应用

介绍三维全波电磁分析软件的算法实现和主要功能,软件包含电磁建模和FDTD数值求解两大模块,可用于射频微波等系统的电磁场分析和设计。自动进行满足FDTD算法条件的均匀和非均匀网格划分,生成描述介质、网格、边界条件、激励源设置等所需的数据文件。通过对目标物体进行平移、旋转、缩放等操作可以进行实时观察与修改,具有较强的电磁建模效率和三维可视化效果,实现了计算区间内电磁场的动态变化演示。通过在实际工程中的应用,以及和商用软件HFSS的对比,验证了软件的功能和应用价值。     

TCP Vegas重选路问题及其解决方法

为克服传统Vegas机制在网络层重选路后可能出现的吞吐量劣化问题,提出了一种称为“主动激励”的新机制。该机制的基本思想是：当TCP拥塞窗口（cwnd）稳定在某个平衡点上时,源端主动地增加基准往返时延,以打破这种平衡,激励Vegas进行窗口调整,通过Vegas自身的窗口调整机制使cwnd达到一个新的平衡,进而对Vegas连接的吞吐量进行有效的恢复。“主动激励”机制并不修改Vegas算法且开销很小,可作为一个独立模块内嵌到Vegas或其增强算法中,从而可以容易地对这些算法进行扩充。     

一种多接口多信道VANET动态频谱分配算法研究

为了实现多接口车载自组织网络(VANET)车辆节点之间通信频谱的动态分配,提出了一种基于信道反馈的动态频谱分配算法。在图论着色模型的基础上,分析信道的质量情况,定义了信道反馈矩阵,车辆节点可以根据信道反馈矩阵中元素的值来自主选择可用信道,从而实现了信道的最大化利用。通过软件仿真比较,可以看出该算法实现了频谱的动态分配,在兼顾最大化系统总收益的前提下大幅度减少了算法的时间开销,显著提高了多接口多信道V ANET的网络性能。     

基于小波变换的语音同步方法研究

为提高语音通信干扰效果客观评估中标准语音文件和受扰语音文件的同步精度,对军标GJB4405A-2006中规定的标准语音文件增设了高低频交叉的双音频同步头;介绍了小波消噪的原理,利用2层sym小波对受扰语音文件进行了消噪处理,将同步头中的高频信号作为噪声进行大幅削弱;设计了基于幅度比较的同步算法,找出同步基准点,并对20组受扰严重的语音文件的进行了仿真测试,仿真结果证明了该方法得到的同步精度符合后续数据处理的要求。     

基于能量分离的混叠语音干扰滤除方法研究

背景噪声中的其他语音干扰在时域和频域上与有用语音完全混叠在一起,用普通的频分技术无法将其去除。本文从能量角度入手,利用调频-调幅模型和共振峰特征,滤除混叠在有用语音中的含绝大部分能量的元音干扰。最后对该方法在MATLAB中进行了仿真验证,并给出了仿真结果。     

Bandwidth extension of telephone speech using magnitude spectrum data hiding

Public telephone systems transmit speech across a limited frequency range, about 300–3400 Hz, called narrowband (NB) which results in a significant reduction of quality and intelligibility of speech. This paper proposes a fully backward compatible novel method for bandwidth extension of NB speech. The method uses magnitude spectrum data hiding technique to provide a perceptually better wideband speech signal. Code excited linear prediction parameters are extracted from the down sampled frequency shifted version of the high frequency components of speech signal existing above NB, which are spread by using pseudo-noise codes, and are embedded in the low amplitude high-frequency regions of the magnitude spectrum of NB speech signal. The embedded information is extracted at the receiving end to reconstruct the wideband speech signal. Theoretical and simulation analyses show that the proposed method is robust to quantization and channel noises. The comparison category rating listening and log spectral distortion tests clearly show that the reconstructed wideband signal gives a much better performance in terms of speech quality when compared to some of the existing speech bandwidth extension methods employing data hiding.     

Speech synthesis of emotions using vowel features of a speaker

Recently, methods for adding emotion to synthetic speech have received considerable attention in the field of speech synthesis research. We previously proposed a case-based method for generating emotional synthetic speech by exploiting the characteristics of the maximum amplitude and the utterance time of vowels, and the fundamental frequency of emotional speech. In the present study, we propose a method in which our reported method is further improved by controlling the fundamental frequency of emotional synthetic speech. As an initial investigation, we adopted the utterance of a Japanese name that is semantically neutral. By using the proposed method, emotional synthetic speech made from the emotional speech of one male subject was discriminable with a mean accuracy of 83.9 % when 18 subjects listened to the emotional synthetic utterances of “angry,” “happy,” “neutral,” “sad,” or “surprised” when the utterance was the Japanese name “Taro,” or “Hiroko.” Further adjustment of fundamental frequency in the proposed method made a much clearer impression on the subjects for emotional synthetic speech.     

Speech Dereverberation Based on Maximum-Likelihood Estimation With Time-Varying Gaussian Source Model

Distant acquisition of acoustic signals in an enclosed space often produces reverberant components due to acoustic reflections in the room. Speech dereverberation is in general desirable when the signal is acquired through distant microphones in such applications as hands-free speech recognition, teleconferencing, and meeting recording. This paper proposes a new speech dereverberation approach based on a statistical speech model. A time-varying Gaussian source model (TVGSM) is introduced as a model that represents the dynamic short time characteristics of nonreverberant speech segments, including the time and frequency structures of the speech spectrum. With this model, dereverberation of the speech signal is formulated as a maximum-likelihood (ML) problem based on multichannel linear prediction, in which the speech signal is recovered by transforming the observed signal into one that is probabilistically more like nonreverberant speech. We first present a general ML solution based on TVGSM, and derive several dereverberation algorithms based on various source models. Specifically, we present a source model consisting of a finite number of states, each of which is manifested by a short time speech spectrum, defined by a corresponding autocorrelation (AC) vector. The dereverberation algorithm based on this model involves a finite collection of spectral patterns that form a codebook. We confirm experimentally that both the time and frequency characteristics represented in the source models are very important for speech dereverberation, and that the prior knowledge represented by the codebook allows us to further improve the dereverberated speech quality. We also confirm that the quality of reverberant speech signals can be greatly improved in terms of the spectral shape and energy time-pattern distortions from simply a short speech signal using a speaker-independent codebook.      

基于三种估计准则下的语音增强方法

语音信号在产生、传输和接收过程中不可避免要受到各种噪声的干扰.为了提高语音清晰度和可懂度,减轻听觉疲劳,增强语音识别效率,需要对带噪声语音进行降噪处理.语音增强技术在语音通信和语音识别过程中有重要价值.在简要介绍语音增强技术的基础上,详细论述了联合最大后验概率估计准则、最大后验概率估计准则和最小均方误差估计准则下的频域语音增强方法的原理及特点,并提出了一种噪声谱估计方法,然后对几种语音增强方法进行了实验仿真.实验证明,基于最小均方误差估计准则的增强方法的效果最好,基于最大后验概率估计准则的增强效果较差.     

A Speech Enhancement Algorithm Based on a Chi MRF Model of the Speech STFT Amplitudes

A speech enhancement algorithm that takes advantage of the time and frequency dependencies of speech signals is presented in this paper. The above dependencies are incorporated in the statistical model using concepts from the theory of Markov Random Fields. In particular, the speech short-time Fourier transform (STFT) amplitude samples are modeled with a novel Chi Markov Random Field prior, which is then used for the development of an estimator based on the Iterated Conditional Modes method. The novel prior is also coupled with a ‘harmonic’ neighborhood, which apart from the immediately adjacent samples on the time frequency plane, also considers samples which are one pitch frequency apart, so as to take advantage of the rich structure of the voiced speech time frames. Additionally, central to the development of the algorithm is the adaptive estimation of the weights that determine the interaction between neighboring samples, which allows the restoration of weak speech spectral components, while maintaining a low level of uniform residual noise. Results that illustrate the improvements achieved with the proposed algorithm, and a comparison with other established speech enhancement schemes are also given.      

基于MFCC的频谱重构实现音高估计和发声分类

音高估计和发声分类可以帮助快速检索目标语音,是语音检索中十分重要且困难的研究方向之一,对语音识别领域具有重要的意义。提出了一种新型音高估计和发声分类方法。利用梅尔频率倒谱系数（MFCC）进行频谱重构,并在对数下对重构的频谱进行压缩和过滤。通过高斯混合模型（GMM）对音高频率和滤波频率的联合密度建模来实现音高估计,实验结果在TIMIT数据库上的相对误差为6.62%。基于高斯混合模型的模型也可以完成发声分类任务,经试验测试表明发声分类的准确率超过99%,为音高估计和发声分类提供了一种新的模型。     

数字语音信号的非均匀采样的实现

该文主要叙述数字语音信号的基于FFT的非均匀采样的技术实现问题,涉及WAV文件格式、分段、对其进行非均匀降速重采样频率的选择原则及生成和语音信号重构,并对实际的WAV格式的数字语音文件用VC++6.0编写的程序实现了基于FFT的重采样;分析方法、实现程序不仅对WAV格式的数字语音文件有效,而且也适用于其它格式的数字语音文件和非语音信号的非均匀采样的实现。     

Bayesian marginal statistics for speech enhancement using log Gabor wavelet

This paper deals with single-channel speech enhancement technique. Initially, the suitability of Log Gabor Wavelet (LGW) is investigated in speech enhancement approach and a novel speech enhancer by Bayesian Maximum a Posteriori (MAP) based Marginal Statistical Characterization (MSC) is developed. The LGW filters are traditional choice for obtaining localized frequency information and these offer the best simultaneous localization of time and frequency information. The MSC is applied in each scale of the LGW, that means a level dependent shrinkage rule is taken to suppress the background perturbations. The pdf of the LGW filtered speech coefficient is modeled with Generalized Laplacian Distribution (GLD), which allows a high approximation accuracy for Laplace distributed real and imaginary parts of the speech coefficients. The robustness of the proposed framework is tested on NOIZEUS speech corpus against seven different established speech enhancement algorithms. Experimental results show that the proposed estimator yield a higher improvement in Segmental SNR (S-SNR), lower Log Area Ratio (LAR) and Weighted Spectral Slope (WSS) distortion compared to existing speech enhancement algorithms.