一种用于WI语音编码的相位预测式矢量量化方法

在传统的低比特率语音编码中,考虑到人耳对相位信息不敏感而经常忽略相位信息,这将导致语音粗糙、刺耳甚至音调发生改变。为了获得高质量的声码器,语音的相位信息是不能不考虑的。该文在散布相位矢量量化方法的基础上进一步去除了相位冗余,在波形内插(Waveform Interpolation,WI)编码模型中对相邻帧慢渐变波形(Slowly Evolving Waveform,SEW)的相位谱差值进行预测式矢量量化。实验发现,该方法大大改善了重建语音效果,明显提高了语音的自然度和清晰度。主观A/B测试结果显示,该方法与固定相位法相比,经4~6 bit的相位量化可使合成语音质量得到显著的改善,相比散布相位矢量量化方法,女声的语音合成质量有所改进。     

低速率WI编码器中4~6bit基音量化算法研究

基音在语音编码中通常采用7bit无失真均匀量化。由于浊音段语音的基音普遍具有缓慢渐变的特点,为了更有效地去除前后帧基音之间存在的相关性,该文基于Eriksson和Kang提出的4bit基音量化算法,针对汉语语音进行研究,实现了一套4~6bit基音量化算法。该算法计算简单,无需码书存储。将此基音量化方案应用于WI模型和WI编码器,主观A/B听力测试结果表明,该方案在高效量化基音的同时保证了合成语音质量几乎没有损失,完全满足低速率WI编码器对量化基音的要求。     

语音特征波形的分解与量化

波形内插语音编码模型作为当今最具潜力的低速率语音编码方案之一,因其良好的性能,越来越受到人们的重视。基于波形内插(WI)语音编码算法,全面总结和分析了现存的特征波形分解和量化的方法,这将为该领域的研究人员提供重要的参考。     

波形内插语音编码技术回顾与展望

介绍了原型波形内插和特征波形内插算法的基本原理及其实现方法，详细描述了原型波形内插和特征波形内插语音编码技术的研究进展情况，并提出了今后的研究方向。     

混合型中速率语音编码系统

设计了一种新型的中速率混合语压编码系统，该系统把语音分割成基带（０．３～１ｋＨｚ）和高频部分（１～３．４ｋＨｚ）对于重要的基带信号，采用高质量的４ｂｉｔ／样点的ＡＤＰＣＭ技术；对于相对次要的高频信号，采用高效的ＶＱ（矢量量化）技术，以压缩码率，对于矢量量化，还提出了一种新的快速算法，通过某种预处理使得搜索码本的速度提高１０倍以上，且质量等效于全搜索方法，本系统具有实现简单，时延短的特点，且主观质量     

波形内插语音编码器中快渐变波形幅度的量化

在低速率参数语音编码算法中,如何用有限的比特数有效地量化幅度谱是一个关键问题。本文对波形内插语音编码模型中快渐变波形幅度的量化问题进行了深入研究和分析,提出了一种基于矢量变维和DCT的REW幅度感觉加权量化方案,该方法降低了编码比特数,减少了存储和计算复杂度,增强了编码语音的感性质量。主观听力测试结果表明该量化方案在每帧4比特时的WI语音编解码质量要优于用基于DCT的REW幅度矩阵量化方案在每帧10比特时的重建语音质量。     

低速率WI语音编码器中LP残差信号的SVD分解及量化

波形内插语音编码模型作为当今最具潜力的低速率语音编码方案之一,因其良好的性能,越来越受到人们的重视.本文在波形内插语音编码算法基础上,提出了一种基于奇异值分解(SVD)的LP残差信号的分解与量化方法,减少了算法的延时,提高了分解精度.在分解模型中,将CW分成基本矩阵、过渡矩阵和补充矩阵,并采用不同的量化方法,有效地降低了运算复杂度;在量化过程中,引入周期因子和能量熵来衡量CW周期程度,解决了奇异值分解后参数难于量化的问题,提高了编码效率.主观A/B测试表明,本文提出的2.4kbpsSVD-WI编码器的重建语音质量略好于2.4kbpsMELP编码器.     

中低速率语音波形编码

提出了一种波形编码的新方法，阐述了其编码原理。该编码算法简单，恢复的语音质量较好。由于利用了矢量量化技术，所以该编码速率较低。     

矢量量化在语音波形编码中的应用

矢量量化(VQ)技术是近几年发展起来的一种高效数据压缩技术.本文介绍了VQ技术的发展历史、现状和它的基本原理,较为详细地讨论了基本矢量量化器的实用设计方法——LBG算法,并对原有的LBG算法进行了改进,给出了实验结果.     

基于离散余弦变换的波形内插语音编码算法

 针对波形内插(Waveform Interpolation,WI)语音编码的特征波形分解问题,本文首先提出了基于离散余弦变换(Discrete Cosine Transform,DCT)的特征波形分解方法,避免了复杂的特征波形对齐运算;其次,针对WI的相位重建问题,提出了清/浊音相位判决和浊音相位分类的方法,提高了重建语音质量;最后,分别构建了速率为2.0kbps和1.6kbps的DCT-WI声码器.主观MOS分表明,2.0kbps的DCT-WI声码器质量优于2.4kbps MELP声码器,1.6kbps的DCT-WI声码器亦取得了良好的听觉效果.     

Fourier Transform Vector Quantization for Speech Coding

Design algorithms and simulation results are presented for vector quantizers for Fourier transformed data. Transforming the data prior to quantization has two potential advantages. First, each sample in the transform domain depends on many samples in the original domain. Thus, even scalar quantization in the transform domain is a form of vector quantization or block source coding in the original waveform domain and the basic coding theorems of information theory show that such block codes can provide better performance than scalar codes, even for memoryless sources. Second, vector quantization of Fourier transformed speech waveforms provides distinctly better subjective quality than ordinary vector quantization of the waveform using codes of comparable complexity. While the system is, of course, more complicated due to the need to take Fourier transforms, its envisioned application is as a coder for the output of FFT chips currently available or under development. The proposed implementation of a Fourier transform vector quantizer (FTVQ) uses a product code structure, providing different codes for different coefficient vectors corresponding to different frequency bands. This is a form of subband coding and yields a simple means of optimizing bit allocations among the subcodes. Two coding structures with corresponding distortion measures are considered: those that quantize vectors of pairs of real and imaginary coefficients and those that quantize separate vectors of magnitude and phase coefficients. Both structures yield good performance for the given complexity in comparison to waveform vector quantizers. For speech coding, a magnitude-phase FTVQ yields better subjective quality than a real-imaginary FTVQ when the rate allocation is properly chosen.     

Gain-Adaptive Vector Quantization with Application to Speech Coding

The generalization of gain adaptation to vector quantization (VQ) is explored in this paper and a comprehensive examination of alternative techniques is presented. We introduce a class of adaptive vector quantizers that can dynamically adjust the "gain" or amplitude scale of code vectors according to the input signal level. The encoder uses a gain estimator to determine a suitable normalization of each input vector prior to VQ encoding. The normalized vectors have reduced dynamic range and can then be more efficiently coded. At the receiver, the VQ decoder output is multiplied by the estimated gain. Both forward and backward adaptation are considered and several different gain estimators are compared and evaluated. Gain-adaptive VQ can be used alone for "vector PCM" coding (i.e., direct waveform VQ) or as a building block in other vector coding schemes. The design algorithm for generating the appropriate gain-normalized VQ codebook is introduced. When applied to speech coding, gain-adaptive VQ achieves significant performance improvement over fixed VQ with a negligible increase in complexity.     

Lattice Vector Quantization Applied to Speech and Audio Coding

Lattice vector quantization(LVQ) has been used for real-time speech and audio coding systems.Compared with conventional vector quantization,LVQ has two main advantages:It has a simple and fast encoding process,and it significantly reduces the amount of memory required.Therefore,LVQ is suitable for use in low-complexity speech and audio coding.In this paper,we describe the basic concepts of LVQ and its advantages over conventional vector quantization.We also describe some LVQ techniques that have been used in speech and audio coding standards of international standards developing organizations(SDOs).     

一种基于改进的矢量量化技术的语音波形编码

针对GLA(Generalized Lloyd Algorithm)对初始码书的敏感性,用PNN(成对最近邻)算法训练初始码书,并将该改进措施用于语音波形编码。实验证明,此改进措施有助于克服GLA对初始码书的敏感性,并且语音恢复效果良好,失真度较低。     

LD-CELP语音编码算法中矢量量化过程的改进

本文介绍了LD-CELP算法的基本原理,在分析其编码的矢量量化过程的基础上提出了一种旨在提高编码速度的改进方案.模拟实验表明,改进算法的编码速度平均提高了一倍.虽然信噪比有所下降,但下降幅度仅为1.2dB,依然能够保证编码质量.     

Application of a VLSI Vector Quantization Processor to Real-Time Speech Coding

The major obstacle which has limited the use of Vector Quantization (VQ) for real-time speech coding is the computationally demanding codebook-search algorithm. The essential task of this algorithm, pattern matching, has several properties which make it amenable to VLSI realization using a highly concurrent processor architecture. A VLSI pattern-matching chip provides the essential building-block for a specialpurpose codebook-search processor (CSP). The CSP can serve as a generic architecture for a variety of VQ-based speech coding applications. This paper reports on a working VQ processor for speech coding based on a first generation VLSI chip that efficiently performs the essential pattern-matching operation needed for the codebook-search process. Furthermore, the CSP architecture, using this chip, has been successfully incorporated into a compact single-board Vector PCM implementation which operates at rates between 7 and 18 kbits/s. A real-time Adaptive Vector Predictive Coder system using the CSP and augmented by a TMS-32010 programmable signal processor has been designed and recently implemented. We describe the structure of these two VQ coders and present experimental results obtained using the single-board Vector PCM coder.     

Hybrid Adaptive Quantization for Speech Coding

This correspondence describes a new quantization technique called hybrid adaptive quantization (HAQ) that uses instantaneous [1], [2] as well as syllabic [3] adaptation of the step size. Two types of instantaneous adaptive algorithms have been used-Jayant's adaptive quantizer (JAQ) and the incremental adaptive quantizer (IAQ). Computer simulations have been performed for a sine-wave, correlated Gaussian signal and digitized speech. Signal-to-noise ratio (SNR) computation for PCM and DPCM coders indicates that the hybrid technique is superior to the normal adaptive quantizer, when both have the same ratio of maximum to minimum step size.     

Image Sequence Coding Using Vector Quantization

In this paper, a new interframe coding technique based upon vector quantization is presented. This algorithm has as its basis twodimensional block vector quantization, at the frame level, onto which is grafted the concept of adaptive codebook replenishment and frame replenishment. This algorithm includes three strategies: label replenishment, label replenishment with mean shift, and label replenishment with mean shift and selective codeword replacement. The last two strategies efficiently update the codebook to track the changes in local statistics on a frame basis. Compared with three-dimensional block vector quantization [16], for bit rates between 0.5 and 0.65 the normalized mean Squared error (NMSE) is reduced by a factor ranging between 2 and 2.5 by the use of replenishment.