基于线性预测残差倒谱的基音检测

传统的基音检测算法对含噪语音的检测结果较差,本文在线性预测倒谱方法的基础上采用基于线性预测残差倒谱的方法对含噪语音进行基音检测。实验结果表明,该方法使基音峰点更加清晰,提高了基音检测的精度。     

一种高效语音基音检测方法的研究

根据语音信号产生的机理,综合考虑基音频率检测的准确性与高效性,提出了一种基于下采样的基音检测方法.该方法以传统的自相关函数法为基础,结合线性预测逆滤波及下采样技术,有效地提高了抗噪性,降低了计算复杂度.通过Matlab软件对计算结果进行仿真分析,证明新算法计算简便、准确,基音频率平滑性好,算法鲁棒性好,适用于实时语音信号的基音检测.     

基于线性预测编码与AMDF的高精度基音检测算法

根据语音信号产生原理,结合线性预测编码(LPC)与平均幅度差函数法(AMDF),提出了一种高精度的基音检测算法。该算法首先利用线性预测分析提取残差信号;然后采用累积平均归一化差分函数与差分信号修正,使基音周期的谷值点更加尖锐;最后利用二次函数拟合与基音周期的倍数检查筛选候选值,得到了准确的基音周期。实验结果表明,与传统方法相比, 该算法的基音检测效果有了明显改善,减少了基音检测中的半频错误,在高信噪比下具有良好的准确性和鲁棒性。     

一种基于基音预测的信息隐藏算法

针对低速率语音编码问题,提出一种基于基音预测的信息隐藏算法。在基音预测编码过程中,采用控制基音闭环搜索的自适应码本搜索范围方法,实现秘密信息的嵌入,在进行语音压缩的同时完成信息隐藏。实验结果证明,该算法具有良好的隐蔽性,且计算复杂度较低。在编码标准G.729a中,秘密比特信息嵌入速率统计平均最高可达374.636 bit/s,PESQ恶化改变率在10.4%以内,检测正确率在66%左右。     

基于线性预测技术的语音基音检测

针对语音基音检测易受加性背景噪声和共振峰干扰的问题,提出了一种基于线性预测技术的语音信号基音检测算法。该算法在维纳滤波基础上,利用线性预测(LPC)技术得到预测残差信号,再对其做自相关(ACF)和平均幅度差(AMDF),得到基音的检测结果。其检测效果比单一自相关函数法和平均幅度差函数法有明显的改善。     

基于线性预测和最大似然的基音检测算法

根据语音信号产生机理，结合常用的线性预测和最大似然法，提出了一种有效的基音检测算法。该算法采用频域分块估计候选基音周期的范围，提高了算法的计算速度。仿真实验表明，该算法与传统方法相比其基音检测结果有了明显的改善，克服了随机错误及倍频、半频错误，在低信噪比下鲁棒性较好。     

基于线性预测残差倒谱的基音周期检测

提出一种基于线性预测残差倒谱的基音周期检测算法.该算法对语音信号的线性预测残差信号做倒谱变换,将其作为基音检测特征.并综合残差倒谱峰、短时能量和短时过零率三种特征,构造一个清浊音判决函数,简化清浊音判决过程,提高判决精度.在基音周期检测过程中,根据基音连续原则,提出峰值重定位方法,有效降低基音倍频和半频的错误率.对比实验表明,本文算法的性能不仅较之传统的倒谱方法有明显改善,同时也优于目前效果较好的YIN算法和多尺度小波算法.     

基于自相关和倒谱法的基音检测改进算法

语音信号基音周期检测一直以来都是语音信号处理的关键技术和热点领域。对传统的基音检测方法进行研究分析,提出基于自相关和倒谱法的基音检测改进算法。先将语音信号进行最小均方误差(LMS)自适应滤波和非线性处理进行语音增强,后进行自相关法和倒谱法加权平方运算来检测基音周期。经Matlab实验仿真,该算法在低信噪比环境中能精确检测基音周期,较传统基音检测方法鲁棒性更好、更精确。     

一种基于小波包变换加权自相关的基音检测算法

噪声环境下的基音检测在语音信号处理中占有重要地位。为了有效提取低信噪比情况下的语音基音周期,提出了一种基于小波包变换加权线性预测自相关的检测方法。该方法首先利用小波包自适应阈值消除噪声,将多级小波包变换的近似分量求和以突出基音信息,并采用小波包系数加权线性预测误差自相关的方法突出基音周期处的峰值,提高了基音周期检测的精度。实验结果表明,与传统的自相关法、小波加权自相关法相比,该方法鲁棒性好,基音轨迹平滑,具有更高的准确性,即使在信噪比为-5dB时仍能取得较为理想的结果。     

对LPC-10编码算法的分析与改进

本文在介绍语音信号产生模型和线性预测理论的基础上,分析了LPC-10的编解码算法,并对算法中涉及的二元激励和基音周期的提取方法进行了改进。     

基于谱减法的基音检测算法

基音周期是语音信号的一个重要参数,它在多个领域有着广泛的应用。提出了一种基于谱减法的基音检测算法：先用谱减法对带噪语音去噪,然后再求语音LPC预测残差的自相关函数及自相关函数的倒谱。仿真结果表明,利用这种改进算法做基音周期检测,检测效果会比传统倒谱检测方法有明显改善。     

Innovative wavelet based speech model using optimal mother wavelet generated from pitch synchronous LPC trajectory

The paper proposes an innovative technique for generation of optimal mother wavelet using LPC trajectory with special reference to speech recognition. A new wavelet based model is proposed for speech signal processing. Lower order linear predictor coefficients (LPC) are related to the vocal tract area near lip that is the articulating organ. The trajectory of second LPC is proposed for the generation of mother wavelet for speech recognition. The observation interval is selected as the pitch period that represents one complete cycle of speech waveform. LPC of order 10 are evaluated for each pitch synchronous (PS) segment. An innovative technique is proposed for the generation of mother wavelet. The mother wavelet is separately generated for each word utterance. This generates a multidimensional space for speech words and increases the recognition accuracy. The wavelet transform (WT) coefficients are evaluated with respect to the generated mother wavelet for each word utterance and are stored as template along with the generated mother wavelet for each word utterance. The data base consists of 30 word utterances recorded locally using the sound recorder facility. In the recognition mode, the external word utterance is scanned and is divided into PS segments. The trajectory of second LPC is tracked. WT coefficients are evaluated with respect to the mother wavelet of each word in the vocabulary and are compared with the template for each word. The results indicate 100% recognition accuracy.     

一种无门限U/V判决和基音检测算法

在实验研究自相关法（ACF）和平均幅度差法（AMDF）基音检测性能的基础上,提出了一种无门限清/浊音判决和基音检测算法。该算法对语音帧分别计算AMDF和LPC残差信号的自相关（LACF）,比较两种方法所得的基音,得出清/浊音判决结果和浊音帧的基音周期。只用一次逻辑判断,无需比较门限;在多种声码器上应用该算法进行语音编/解码仿真实验,表明判决和检测算法具有较高的准确性和较强的噪声鲁棒性。     

基音周期估计算法在声调康复训练中的应用

汉语是一种声调语言,而声调的识别是以基音周期的估计为基础的。文章首先用LPC求残差系数,然后对残差系数求自相关周期,进而求出基音周期,再结合计算机智能诊断,提出了一套适合临床语言障碍患者进行语音诊断和康复训练的方法。     

基于LPC残差与SCMDSF相结合的基音周期检测

基音周期检测一直是音频处理领域的研究热点,基音周期的精确检测实际上是一件比较困难的事情。提出了一种LPC残差与SCMDSF相结合的基音周期检测,该算法的特点在于着重对被处理的语音进行滤波预处理,提取语音信号的LPC残差,消除了声道响应信息,对求出的语音残差信号做SCMDSF计算,并求出语音的基音周期。实验表明,在噪声环境下这种处理方法能够比较准确的提取基音周期。     

An efficient perceptual hashing based on improved spectral entropy for speech authentication

Combined with the linear prediction-minimum mean squared error (LP-MMSE), an efficient perceptual hashing algorithm based on improved spectral entropy for speech authentication was proposed in this paper. The linear prediction analysis is conducted on speech signal after preprocessing, framing and adding windows, and obtained the minimum mean squared error coefficient matrix. And then, the spectral entropy parameter matrix of each frame is calculated by using improved spectral entropy method. And the final binary perceptual hashing sequence is generated based on the above two matrices, and the speech authentication is completed. Comparing the experimental results of combining the Teager energy operator (TEO) with the linear predictive coefficients (LPC), LP-MMSE and line spectrum pair (LSP) coefficient respectively, it can be seen that the proposed algorithm had a good compromise between robustness, discrimination and authentication efficiency, and the proposed algorithm can meet the requirement of real-time speech authentication in speech communication. Experimental results show that the proposed algorithm was better than other existing methods in compactness.     

声门激励信号的获取及其应用

声门激励信号是语音信号的源信号,可用于语音特征参数的有效提取。研究了从观测语音获取声门激励的两种方法——线性预测法和倒谱法;用实际录制的语音做计算机仿真实验,比较了两种方法的性能和特点。结果表明倒谱法获取声门激励、由它提取基因周期等激励特征参数的精度高,但计算量相对较大;线性预测法由于采用高效算法,不仅获取声门激励的速度快,而且可同时获取声道模型参数、语音功率谱等重要参数,是获取声门激励的常用方法。     

语音清浊音分类及浊音谐波提取算法--三阶累积量基于正弦语音模型的应用

在低信噪比和非平稳噪声干扰下,语音信号的清浊音检测是语音信号处理中的一个重要研究问题。论文基于语音正弦模型,提出了一种清浊音分类和浊音谐波提取算法。该方法在分析了语音的三阶累积量谱后,用子谐波-谐波方法取得基音,并计算出谐波参数和高低频能量比值。它利用谱包络估计器得到谱包络及尖峰信号,结合最小均方估计准则下的迭代算法计算语音谐波的信噪比;通过对上面各计算结果的综合评价得出语音帧的浊音度,从而得到语音清浊音的分类和浊音谐波数。仿真结果表明,该算法在复杂噪声背景下,能有效进行语音分类,准确得到浊音度。同时该算法还具有实时性好、语音参数分析精度高的特点。     

A GEOSTATISTICAL MODEL FOR LINEAR PREDICTION ANALYSIS OF SPEECH

This paper presents a geostatistical model as a new approach to the linear prediction analysis of speech. The autocorrelation method of autoregressive modeling, which is widely applied in the linear predictive coding of speech, is used as a benchmark for comparison with the present algorithm. Before discussing the proposed model, we will briefly describe the concepts of linear prediction analysis of speech and how this is solved by the well-known method of autocorrelation. Following is the introduction of geostatistics including the ideas of regionalized variables, semi-variograms and kriging equations. We then propose a geostatistical model to the linear prediction modeling of speech signals. Examples on speech data are given to illustrate the effectiveness of the present algorithm in comparison with the autocorrelation method. Advantages offered by the proposed geostatistical algorithm over the autocorrelation method in the linear prediction analysis of speech are summarized as follows: (1) it is more effective due to the optimization of the kriging equations taking into account the biased condition; (2) it is more flexible by allowing different biased values for the fitting of the signal spectrum, and therefore may provide a means for adaptive LPC; (3) it can give a good estimate of the number of poles used in the LPC by means of the theoretical semi-variogram.