基于自相关平方函数与小波变换的基音检测

在背景噪声干扰条件下,研究语音信号的基音周期,提出了一种基于自相关平方函数与小波变换结合的基音检测算法。该算法先用小波变换对带噪语音去噪,然后再求语音的自相关平方函数以突出真实基音周期的峰值,以获取较精确的基音周期。实验结果表明,与传统的自相关法相比,该算法鲁棒性好,具有更高的准确性,且计算复杂度低,利于语音合成和编码的实时处理。     

基于改进小波变换的语音基音周期检测

基音在许多方面都有比较广泛的应用,比如语音编码、语音识别、语音转换、音乐检索以及发声系统疾病诊断等。针对目前很多小波变换方法在测量基音周期时存在的准确度低、复杂度高、鲁棒性差等缺点,以及在带噪语音环境下,特别是在非平稳噪声下比较难判断语音基音周期的问题,提出了一种基于改进小波变换的语音基音检测方法。首先将每帧带噪信号进行预处理,提取出有话段的信息,消除直流分量;然后在加窗分帧后先进行端点检测,滤波后再分帧;接着再利用小波分解后取低频系数重构信号;最后结合四阶累积法对重构信号进行基音检测。试验结果表明,该方法在不同带噪语音环境下和低信噪比条件下,提高了带噪语音基音检测的准确性。与传统的小波变换法相比,该方法鲁棒性好且计算复杂度低,有利于语音基音周期检测。     

结合改进自相关与加权小波分量的基音检测法

自相关函数法和小波变换法是经典的基音检测方法,在简要分析单独使用它们进行基音检测存在不足的基础上,提出一种结合改进自相关与加权小波分量的检测方法。采用改进自相关函数对传统自相关函数进行幅度补偿以弥补传统自相关函数随滞后时间增加导致幅度衰减的缺陷;将多级小波变换分量加权求和以突出语音的基音信息,然后将两种方法结合突出真实基音周期处的峰值。实验结果表明,与传统的自相关函数法和小波变换法相比,两者结合的方法减少了倍频、半频及伪随机点的错误,提高了基音检测的精度。     

一种有效的实时基音检测算法

针对基音周期检测实时性的要求,提出了基于小波变换的实时语音基音周期检测算法。该算法利用小波变换极值与信号突变点之间的关系,将小波域波形与时域波形相结合,采取自适应基准、多特征参数提取小波系数极大值,并在2.5ms时间内捕捉并检测到新的基音脉冲位置。实验表明,该算法对语音和残差信号取得了较好结果。     

基于小波变换和动态验证的基音检测算法

针对基音周期检测中容易出现的半周期和倍周期错误,综合考虑了常用的小波变换和短时自相关方法的优缺点,以及相邻基音周期长度的渐变性,提出了把两者相结合的基音周期检测算法.对语音信号进行清浊音检测和前置带通滤波,利用小波变换方法进行初步检测,对基音周期变化过大的情况使用自相关方法进行验证.实验结果表明,该方法在不同信噪比下的基音周期检测准确率都明显高于普通的小波变换检测方法.同时,该方法还有助于通过人工方式快速修正基音周期.     

基于变步长LMS减噪的基音检测改进算法

针对噪声环境下传统的基音检测算法精度不高的问题,提出一种改进的基音检测算法。采用变步长最小均方(LMS)自适应滤波器对带噪语音信号进行减噪,计算减噪后语音的自相关函数(ACF)归一化值、改进平均幅度差函数(MAMDF)、倒谱,建立非线性组合函数,突出基音周期处的峰值。基于置信区间和相邻语音帧的基频差值进行平滑处理,减少基音提取的错误。仿真结果表明,与传统算法相比,该算法检测准确率提高了至少6.7%,在低信噪比环境下鲁棒性也明显提高。     

语音信号基音检测的现状及展望

基音检测是语音处理中的一个非常重要的问题,但由于影响基音检测的因素众多,使得基音周期的准确估计非常困难.文中阐述了短时自相关函数法、短时平均幅度差函数法、倒谱法、小波变换法等几种经典的基音检测方法,分析它们各自的优点及存在的不足,并在预处理、后处理、语音信号的产生模型、语音信号的个性特征、发音时的情感及力度等基音检测的各个环节上提出了一些看法,并就一些可能出现的突破口做了一些展望.     

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。     

语音信号基音检测的现状及展望

基音检测是语音处理中的一个非常重要的问题，但由于影响基音检测的因素众多．使得基音周期的准确估计非常困难。文中阐述了短时自相关函数法、短时平均幅度差函数法、倒谱法、小波变换法等几种经典的基音检测方法．分析它们各自的优点及存在的不足，并在预处理．后处理、语音信号的产生模型、语音信号的个性特征、发音时的情感及力度等基音检测的各个环节上提出了一些看法，并就一些可能出现的突破口做了一些展望。     

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。     

Pitch estimation of speech and music sound based on multi-scale product with auditory feature extraction

The pitch is a crucial parameter in speech and music signals. However, due to severe noisy conditions, missing harmonics, unsuitable physical vibration, the determination of pitch presents a great challenge when desiring to get a good accuracy. In this paper, we propose a method for pitch estimation of speech and music sounds. Our method is based on the fast Fourier transform (FFT) of the multi-scale product (MP) provided by a feature auditory model of the sound signals. The auditory model simulates the spectral behaviour of the cochlea by a gammachirp filter-bank, and the out/middle ear filtering by a low-pass filter. For the two output channels, the FFT function of the MP is computed over frames. The MP is based on constituting the product of the speech and music wavelet transform coefficients at three scales. The experimental results show that our method estimates the pitch with high accuracy. Besides, our proposed method outperforms several other pitch detection algorithms in clean and noisy environments.     

基于听觉及小波变换的汉语语音调值分析

提出了一种基于听觉与小波变换处理的汉语语音基音的方法，在对听觉系统描述的基础上，给出了人的听觉与小波变换的关系，选取适合汉语事音基频提取的小波函数，给出了基频提取的应用实例和基于ＦＣＭ模糊聚类分析的汉语四声调值识别结果。     

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETSI) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD.     

A wavelet- based transform method for quality improvement in noisy speech patterns of Arabic language

This paper addresses the problem of single-channel speech enhancement of low (negative) SNR of Arabic noisy speech signals. For this aim, a binary mask thresholding function based coiflet5 mother wavelet transform is proposed for Arabic speech enhancement. The effectiveness of binary mask thresholding function based coiflet5 mother wavelet transform is compared with Wiener method, spectral subtraction, log-MMSE, test-PSC and p-mmse in presence of babble, pink, white, f-16 and Volvo car interior noise. The noisy input speech signals are processed at various levels of input SNR range from ?5 to ?25 dB. Performance of the proposed method is evaluated with the help of PESQ, SNR and cepstral distance measure. The results obtained by proposed binary mask thresholding function based coiflet5 wavelet transform method are very encouraging and shows that the proposed method is much helpful in Arabic speech enhancement than other existing methods.     

A perceptually motivated stationary wavelet packet filterbank using improved spectral over-subtraction for enhancement of speech in various noise environments

In this paper, we propose a speech enhancement method where the front-end decomposition of the input speech is performed by temporally processing using a filterbank. The proposed method incorporates a perceptually motivated stationary wavelet packet filterbank (PM-SWPFB) and an improved spectral over-subtraction (I-SOS) algorithm for the enhancement of speech in various noise environments. The stationary wavelet packet transform (SWPT) is a shift invariant transform. The PM-SWPFB is obtained by selecting the stationary wavelet packet tree in such a manner that it matches closely the non-linear resolution of the critical band structure of the psychoacoustic model. After the decomposition of the input speech, the I-SOS algorithm is applied in each subband, separately for the estimation of speech. The I-SOS uses a continuous noise estimation approach and estimate noise power from each subband without the need of explicit speech silence detection. The subband noise power is estimated and updated by adaptively smoothing the noisy signal power. The smoothing parameter in each subband is controlled by a function of the estimated signal-to-noise ratio (SNR). The performance of the proposed speech enhancement method is tested on speech signals degraded by various real-world noises. Using objective speech quality measures (SNR, segmental SNR (SegSNR), perceptual evaluation of speech quality (PESQ) score), and spectrograms with informal listening tests, we show that the proposed speech enhancement method outperforms than the spectral subtractive-type algorithms and improves quality and intelligibility of the enhanced speech.     

二次离散小波变换的语音增强方法

提出了一种基于二次离散小波变换（DWT）的语音增强算法。该算法首先对带噪语音信号进行离散小波变换,提取离散细节信号,并对其进行第二次离散小波变换。再按照不同的规则选取阈值,对信号进行去噪处理。最后再对出来后的语音信号进行合并。对比实验结果表明,该方法具有良好的消除噪声的效果,提高了语音的清晰度和可懂度。