基于时域去相关预处理的卷积混合语音盲分离算法

因语音信号自身的相关性及不平稳性,使卷积混合语音信号的盲分离变得十分困难.本文提出了一种基于时域去相关预处理的卷积混合语音盲分离时域算法.该算法采用自适应格型预测误差滤波器对语音信号进行时域去相关处理,同时基于空域去相关算法完成卷积混合语音分离.该算法充分考虑了语音信号自身的相关性及不平稳性,具有计算量小、收敛速度快、稳定性好的优点.仿真实验验证了该算法在对卷积混合语音信号进行盲分离时的有效性.     

改进的在线自然语音卷积混合信号时域盲分离方法

针对语音信号所具有的非平稳性和时域相关性,提出了一种新的卷积混合语音信号盲分离的在线时域算法。该算法通过利用分块处理方法和带遗忘因子更新的非完备约束条件及其推广,对于许多已有在线算法中存在的由于目标源数目随时间不断变化而产生的不稳定性问题,以及语音信号时域相关性而导致的恢复信号失真问题进行了改进,最后通过仿真,结果表明,本文方法可以有效地处理语音卷积信号的在线盲分离问题,同时在源数目变化时算法的鲁棒性较好。     

基于回溯搜索优化的卷积混合语音盲分离

针对独立矢量分析（IVA）算法初始分离矩阵取值对分离性能影响较大的局限性,提出了基于回溯搜索优化的卷积混合语音盲分离算法。采用频域各频率点IVA分离信号的复数峭度和作为目标函数,利用回溯搜索优化算法（BSA）对初始分离矩阵进行优化调整,更好地实现了语音信号的盲分离。在分离过程中,采用复Givens旋转变换原理将对分离矩阵的求解转化为对旋转角度的求解,有效减少了BSA的参数编码维数,降低了优化求解难度。针对语音信号的卷积混合分离实验表明,该算法具有良好的分离效果,其分离性能较之基本IVA算法显著提升。     

一种基于ICA的同态盲反卷积算法

盲反卷积是图像处理、语音信号处理、通信、系统辨识和声学等许多研究和应用的基本问题,具有重要的理论与应用价值。根据无损检测中盲反卷积问题的特点,提出了一种新的基于ICA的同态盲反卷积算法。该算法首先将检测信号变换到复倒谱域,将卷积混合模型变为线性混合模型,即ICA问题;然后通过ICA将系统冲击响应和输入信号分离;最后,根据分离的复倒谱信号,重构其时域信号。论文提出的盲反卷积算法具有运算量小,计算速度快,分离精度高等特点,且不受信道是否为最小相位信道的影响。计算机模拟和实验数据都证明了算法的有效性。     

一种新的联合块对角化卷积盲分离时域算法

提出一种基于高阶累积量联合块对角化的时域算法求解卷积混合盲信号分离问题。引入白化处理,将混叠矩阵转变成酉矩阵,混合信号转变为互不相关的,进而计算出其对应的一系列高阶累积量矩阵,通过最小化代价函数来实现高阶累积量矩阵联合块对角化的目的,在时域中解决超定卷积盲分离问题。实验表明,相比于经典的自然梯度算法,所提方法的分离精度更高,且运算速度也更快。     

频域盲解卷积局限性分析及一种改进算法

在频域盲解卷积问题中,时域信号的卷积混合转化为频域信号在有限频点的瞬时混合,使算法复杂度大大降低。但这种算法的局限是分离结果存在次序和幅度上的不确定性,并且窗函数长度和信号非平稳性之间存在相互制约的关系。文中对语音信号频域盲解卷积算法存在的制约因素进行分析并提出一种改进的基于包络相关性的排序方法。在分裂谱法的基础上,通过“分裂”后的多路信号求得“总包络”,再依据“总包络”进行排序,从而克服传统的直接依据输出信号包络相关性进行排序的不足。实验结果表明,采用本方法可获得较高的分离质量。     

频域盲解卷积局限性分析及一种改进算法

在频域盲解卷积问题中,时域信号的卷积混合转化为频域信号在有限频点的瞬时混合,使算法复杂度大大降低.但这种算法的局限是分离结果存在次序和幅度上的不确定性,并且窗函数长度和信号非平稳性之间存在相互制约的关系.文中对语音信号频域盲解卷积算法存在的制约因素进行分析并提出一种改进的基于包络相关性的排序方法.在分裂谱法的基础上,通过"分裂"后的多路信号求得"总包络",再依据"总包络"进行排序,从而克服传统的直接依据输出信号包络相关性进行排序的不足.实验结果表明,采用本方法可获得较高的分离质量.     

采用非负矩阵分解的语音盲分离

针对多通路语音信号的欠定卷积混合模型,提出一种基于非负矩阵分解(NMF)的语音盲分离方法。该方法使用高斯分量对源信号的短时傅里叶变换(STFT)进行表示,高斯分量由基于板仓-斋藤(Itakura-Saito(IS))散度的非负矩阵分解的因子所组成。使用极大期望值算法(EM)求解参数,并对信号进行重组。该方法被应用到双声道立体声信号的盲分离实验,实验结果表明了该方法的有效性。     

基于平行因子分解的频域盲解卷积算法

针对卷积混合盲分离问题,文章提出了一张基于张量平行因子分解的盲分离算法。该算法通过将接收信号的频域相关矩阵叠加成三阶张量,再对此三阶张量进行平行因子分解,最后利用基于K-means聚类的全排列解模糊算法来完成无排列模糊的混合矩阵估计。通过仿真实验,计算分离信号与源信号的相似系数,结果表明提出的算法具有很好的分离效果,而且实现简单,可满足实际应用的要求。     

语音增强的计算机仿真

研究关于盲源分离的特征向量分离算法在语音增强的应用,传统的方法对混合的语音信号很难进行有效的分离,而在实际中很多场合都需要对语音信号进行增强.为消除噪音,提高清晰度,使用的盲源分离算法却正能实现传统方法难以实现的技术.运用一种盲源分离的特征向量分离算法来进行语音增强,并且对实际的两个语音信号运用该算法进行了混合语音信号的分离增强实验,利用MATLLAB软件对混合语音信号进行了盲源分离的特征向量分离算法的仿真,可从混合语音信号分离出了两个原始语音信号.证明了盲源分离算法应用于语音分离的可行性,为盲源分离应用于语音增强提供了参考依据.     

基于级联混沌系统的分数域语音加密算法

为保证语音信号在通信传输中的安全性,提出一种基于级联混沌系统的分数域语音加密算法。首先,对语音信号进行分组;其次,利用混沌系统获取分数傅里叶变换的阶次,各组数据对应的阶次呈动态变化;然后,采用具有较低计算复杂度的采样型分数傅里叶离散变换得到各组对应的分数域谱数据;最后,利用级联混沌系统依次对各组分数域进行数据加密,从而实现语音信号的整体加密。实验结果表明,所提算法对密钥具有极大的敏感度,得到的加密信号与原信号相比波形和分数域谱分布更均匀、相关性更小;同时与频域加密和固定阶次的分数域加密方法相比,该算法能有效增大密钥空间,同时降低计算复杂度。可见所提算法能够有效满足语音信号的实时安全传输要求。     

基于DCT变换及SVD处理的音频数字水印算法

提出了一种基于离散余弦变换及奇异值分解的音频水印算法。首先对二值水印图像进行奇异值分解求出奇异值,然后对所得奇异值进行基于音频信号变换域性质的调制,并对音频信号进行离散余弦变换并计算水印嵌入点,最后将经过调制的水印信号嵌入。仿真试验证明这种自适应音频数字水印算法具有稳健性和不可觉察性。     

Improvement to speech-music discrimination using sinusoidal model based features

This paper addresses a model-based audio content analysis for classification of speech-music mixed audio signals into speech and music. A set of new features is presented and evaluated based on sinusoidal modeling of audio signals. The new feature set, including variance of the birth frequencies and duration of the longest frequency track in sinusoidal model, as a measure of the harmony and signal continuity, is introduced and discussed in detail. These features are used and compared to typical features as inputs to an audio classifier. Performance of these sinusoidal model features is evaluated through classification of audio into speech and music using both the GMM (Gaussian Mixture Model) and the SVM (Support Vector Machine) classifiers. Experimental results show that the proposed features are quite successful in speech/music discrimination. By using only a set of two sinusoidal model features, extracted from 1-s segments of the signal, we achieved 96.84% accuracy in the audio classification. Experimental comparisons also confirm superiority of the sinusoidal model features to the popular time domain and frequency domain features in audio classification.     

基于DWT和SVD的自适应数字音频零水印算法

为了对数字音频的版权进行有效的保护,结合人类听觉系统和奇异值分解的重要特性,提出了一种小波域数字音频零水印算法。用混沌序列对水印图像进行加密,根据音频信号的时域局部特征选择最适合于构造零水印的音频段,对选取的音频段进行离散小波变换,提取小波域的低频分量作奇异值分解,利用低频系数的最大奇异值构造零水印,实现数字音频的版权保护。实验结果表明,水印的安全性和不可感知性很好;对于不同风格的音频信号,算法均具有良好的鲁棒性,能够有效抵抗高斯噪声、低通滤波、重采样、重量化、剪切以及压缩等攻击。     

A flexible and scalable audio information retrieval system for mixed‐type audio signals

The content‐based classification and retrieval of real‐world audio clips is one of the challenging tasks in multimedia information retrieval. Although the problem has been well studied in the last two decades, most of the current retrieval systems cannot provide flexible querying of audio clips due to the mixed‐type form (e.g., speech over music and speech over environmental sound) of audio information in real world. We present here a complete, scalable, and extensible content‐based classification and retrieval system for mixed‐type audio clips. The system gives users an opportunity for flexible querying of audio data semantically by providing four alternative ways, namely, querying by mixed‐type audio classes, querying by domain‐based fuzzy classes, querying by temporal information and temporal relationships, and querying by example (QBE). In order to reduce the retrieval time, a hash‐based indexing technique is introduced. Two kinds of experiments were conducted on the audio tracks of the TRECVID news broadcasts to evaluate the performance of the proposed system. The results obtained from our experiments demonstrate that the Audio Spectrum Flatness feature in MPEG‐7 standard performs better in music audio samples compared to other kinds of audio samples and the system is robust under different conditions. © 2011 Wiley Periodicals, Inc.     

基于人类听觉的伪随机序列的信息隐藏技术

提出了一种在话音传递中，蒙蔽人类听觉的信息隐藏模型。给出了数字音频信号中信息隐藏的伪随机序列生成算法。利用此算法对隐蔽信息先加密，再对数字音频信号进行分段处理，并选出部分段做离散余弦变换(DCT)，最后依据人类的听觉系统选择DCT域中高频系数进行量化完成加密的隐蔽信息嵌入。实验结果表明，在提高鲁棒性的同时，还大大增加了安全性，就是在外界发现数字音频含有隐藏信息的情况下，在不知道密钥的情况下，还是很难提取隐蔽信息。     

Detecting and locating digital audio forgeries based on singularity analysis with wavelet packet

Audio watermarking and signature are widely used for authentication. However, these techniques will become powerless in many actual situations because of their requirement of additional information. Audio forensic techniques are necessary for digital audio. In this paper, we propose an audio forensics scheme to detect and locate speech audio forged operations in time domain (including deletion, insertion, substitution and splicing) by performing discrete wavelet packet decomposition and analyzing singularity points of audio signals. We first analyze the forged operations and find that the audio signals will often generate new singular points because of the decrease or breaking of the correlation property of those samples close to the tampering position. Then we utilize the singularity analysis based on wavelet packet and design five parameters (which is different for the sample rate of digital audio file) to propose an approach which can detect and locate audio forgeries in time domain. Finally, extensive experimental results have demonstrated that the proposed method can better achieve the goals that identify whether a given speech file has been tampered (e.g., part of the content deleted or replaced) previously and further locate the forged positions in time domain.     

Blind separation of audio signals using trigonometric transforms and wavelet denoising

This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the discrete cosine transform (DCT) or the discrete sine transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Wavelet denoising of the noisy mixtures is recommended in this paper as a preprocessing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving most of the transform domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations, especially when the separation algorithm is preceded by a wavelet denoising step. The simulation results confirm the superiority of transform domain separation to time domain separation and the importance of the wavelet denoising step.     

Blind separation of audio signals using trigonometric transforms and Kalman filtering

This paper deals with the problem of blind separation of audio signals from noisy mixtures. It proposes the application of a blind separation algorithm on the Discrete Cosine Transform (DCT) or the Discrete Sine Transform (DST) of the mixed signals, instead of performing the separation on the mixtures in the time domain. Kalman Filtering of the noisy separated signals is recommended in this paper as a post-processing step for noise reduction. Both the DCT and the DST have an energy compaction property, which concentrates most of the signal energy in a few coefficients in the transform domain, leaving the rest of the transform-domain coefficients close to zero. As a result, the separation is performed on a few coefficients in the transform domain. Another advantage of signal separation in transform domains is that the effect of noise on the signals in the transform domains is smaller than that in the time domain due to the averaging effect of the transform equations. The simulation results confirm the effectiveness of transform-domain signal separation and the feasibility of the post-processing Kalman filtering step.     

基于高斯混合模型的DCT域水印检测方法

基于音频DCT系数的统计特征,提出了一种水印检测方法.采用扩频的方法在DCT域嵌入水印, 分别对嵌入水印和未包含水印的音频信号在DCT域进行统计学习,得到对应的高斯混合模型(Gaussian mixture model, GMM). 接收端采用最大似然检测,判断是否嵌入水印并提取相应的水印信息. 仿真结果表明本文的水印检测算法对常见的信号攻击具有鲁棒性,与传统的相关检测法相比,检测可靠性更高.