Main melody extraction from polyphonic music based on modified Euclidean algorithm

Extracting main melody from polyphonic music is one of the most appealing and challenging tasks in music information retrieval (MIR). In this paper, a new melody extraction method based on a modified Euclidean algorithm (MEA) is proposed. Firstly, the instantaneous frequency is adopted to gain better frequency discrimination, and the frame-wise pitch candidates are estimated based on the modified Euclidean algorithm. Next, the candidate trajectories are formed using these potential candidates, and padded by the candidate one octave above or below if there is a gap at some isolated frames. Finally, the melodic contours are extracted using the melody smoothness and salience principle. The proposed modified Euclidean algorithm can deal with diverse coprime harmonic combinations, and work well at low computational cost and memory requirement. The experimental results show that the proposed method can extract main melody extraction effectively with few pitch candidates.     

Speaker-independent recognition of Chinese tones

This paper presents a reliable speaker-independent method of recog-nizing Chinese tones.An unbiased center-clipping autocorrelation algorithm ofpitch period extraction is proposed.A two-dimensional decision vector is usedfor recognizing Chinese tones by passing the pitch period sequence through theprocedures of data selection,error correction,data smoothing and curve fitting.The average correct rate of tone recognition for isolated Chinese syllables isover 98%.     

改进LVAMDF及综合多因素基音检测算法

在对语音信号静音、清音、浊音划分的基础上,针对语音信号周期特征明显段分布随机性问题,提出改进的变长度平均幅度差函数LVAMDF及综合多因素基音检测算法,该算法对语音信号进行周期特征明显段和周期特征不明显段的聚类划分,同时,获取周期特征明显语音段的基音周期,针对少数基音周期划分倍频或半频问题,提出识别、修正方法,其识别、修正率极高。在对大量真实语音处理中,能够精确的检测出语音特征明显段的基音周期端点,基本没有倍频和半频划分,并且和AMDF、ACF算法作了对比。     

Harmonic signal extraction from noisy chaotic interference based on synchrosqueezed wavelet transform

For the harmonic signal extraction from chaotic interference, a harmonic signal extraction method is proposed based on synchrosqueezed wavelet transform(SWT). First, the mixed signal of chaotic signal, harmonic signal, and noise is decomposed into a series of intrinsic mode-type functions by synchrosqueezed wavelet transform(SWT) then the instantaneous frequency of intrinsic mode-type functions is analyzed by using of Hilbert transform, and the harmonic extraction is realized. In experiments of harmonic signal extraction, the Duffing and Lorenz chaotic signals are selected as interference signal, and the mixed signal of chaotic signal and harmonic signal is added by Gauss white noises of different intensities.The experimental results show that when the white noise intensity is in a certain range, the extracting harmonic signals measured by the proposed SWT method have higher precision, the harmonic signal extraction effect is obviously superior to the classical empirical mode decomposition method.     

提升小波加权自相关函数的基音检测算法*

随着计算机技术的发展,语音信号处理作为人机交互的重要渠道,其在复杂噪声环境下的特征值检测算法直接关系到计算机的运算效率。基音周期是语音特征值提取的重要参数之一。针对传统基音检测算法在噪声环境下检测精度低的问题,提出了一种基于自适应提升小波变换加权线性预测误差自相关函数的基音检测算法。该方法用多级提升小波近似系数加权求和的方法来弥补自相关函数随着时间延迟量的增加幅值衰减的缺陷;用线性预测误差自相关函数的方法来抑制共振峰的干扰,然后将两种方法结合来突出基音周期处的峰值。实验结果表明,与传统的自相关函数法和小波加权法相比,该方法能有效减弱共振峰的影响,突出基音周期处的峰值,提高基音周期检测精度,鲁棒性更好。     

混沌干扰中基于同步挤压小波变换的谐波信号提取方法

针对混沌干扰背景下多个谐波信号的提取问题, 提出了一种基于同步挤压小波变换(SST)的谐波信号抽取方法. 首先利用SST将混沌信号和谐波信号组成的混合信号分解为不同的内蕴模态类函数, 然后利用Hilbert变换对分离出的内蕴模态类函数进行频率识别, 从中分离出各谐波信号. 以Duffing混沌背景为例, 对混沌干扰下多谐波信号的提取进行了实验分析. 实验结果表明: 对于不同频率间隔的多个谐波分量, 本文方法的提取结果都具有较高的精度, 而且所提方法对高斯白噪声的干扰具有较好的鲁棒性, 综合提取效果优于经典的经验模态分解方法.     

一个快速自动音乐记谱方法

自动音乐记谱是音乐信号处理中的关键技术。本文描述了一个快速的自动复音音乐记谱方法。该方法采用回声器时频分析(RTFI)作为时频分析工具,主要由两个阶段组成,能量基的音符切分和多基频估计。本文所采用的多基频估计方法首先将RTFI能量谱按照谐音组合原理转换为基频能量谱,并基于基频能量谱采用简单的峰拾起方法对基频做初步估计;然后根据频谱不规律性和乐音谐音结构的基本假定,消除初步估计中的错误预测。     

Second generation wavelet transform-based pitch period estimation and voiced/unvoiced decision for speech signals

Pitch detection is an important part of speech recognition and speech processing. In this paper, a pitch detection algorithm based on second generation wavelet transform was developed. The proposed algorithm reduces the computational load of those algorithms that were based on classical wavelet transform. The proposed pitch detection algorithm was tested for both real speech and synthetic speech signal. Some experiments were carried out under noisy environment condition to evaluate the accuracy and robustness of the proposed algorithm. Results showed that the proposed algorithm was robust to noise and provided accurate estimates of the pitch period for both low-pitched and high-pitched speakers. Moreover, different wavelet filters that were obtained using second generation wavelet transform were considered to see the effects of them on the proposed algorithm. It was noticed that Haar filter showed good performance as compared to the other wavelet filters.     

螺纹边界法对圆柱超声缓冲杆尾随脉冲干扰的抑制*

对于高温管道壁厚的超声波在线监测,需要超声波探头与缓冲杆搭配使用,以降低探头的接触温度。超声波在圆柱形缓冲杆的边界会发生波型转换,在缓冲杆的第一次和第二次底面回波之间产生等间隔的尾随脉冲干扰,影响管道内壁回波的识别与提取。该文创新性地采用螺纹边界法改变缓冲杆的边界特征以抑制尾随脉冲干扰,经实验验证螺纹边界法对尾随脉冲干扰有较好的抑制效果;不同螺纹螺距的实验对比表明,对于直径为20 mm的钢材料圆柱缓冲杆,在1 mm、2 mm和3 mm三种螺距中,螺距为2 mm的螺纹边界对5 MHz超声波的尾随脉冲干扰的抑制效果最强。     

混沌背景下信号的盲分离

混沌信号与确定性小信号叠加生成的混合信号是一更高维的混沌信号,因而不能用一般的混沌信号噪声抑制的方法进行分离.提出了一种这类信号盲分离的方法.在重构未知的混沌信号的动力方程时,充分利用混沌吸引子的几何特性,并且限定动力映射为原混沌吸引子所在流形的内部映射,从而保证了重构的动力系统方程对应于原混沌信号,而不是同样具有混沌特性的混合信号.然后利用重构的动力方程,借用混沌信号中的噪声抑制思想,估计出原混沌信号对应的轨道,实现信号分离.通过对Lorenz系统中谐波信号、Henon映象中自回归过程,以及脑电信号中谐波信号进行提取的数值实验,验证了信号盲分离方法的有效性和可行性. 关键词： 混沌 非线性 信号处理 盲分离     

The representation of periodic sounds in simulated sustained chopper units of the ventral cochlear nucleus

The nature of the neural processing underlying the extraction of pitch information from harmonic complex sounds is still unclear. Electrophysiological studies in the auditory nerve and many psychophysical and modeling studies suggest that pitch might be extracted successfully by applying a mechanism like autocorrelation to the temporal discharge patterns of auditory-nerve fibers. The current modeling study investigates the possible role of populations of sustained chopper (Chop-S) units located in the mammalian ventral cochlear nucleus (VCN) in this process. First, it is shown that computer simulations can predict responses to periodic and quasiperiodic sounds of individual Chop-S units recorded in the guinea-pig VCN. Second, it is shown that the fundamental period of a periodic or quasiperiodic sound is represented in the first-order, interspike interval statistics of a population of simulated Chop-S units. This is true across a wide range of characteristic frequencies when the chopping rate is equal to the f0 of the sound. The model was able to simulate the results of psychophysical studies involving the pitch height and pitch strength of iterated ripple noise, the dominance region of pitch, the effect of phase on pitch height and pitch strength, pitch of inharmonic stimuli, and of sinusoidally amplitude modulated noise. Simulation results indicate that changes in the interspike interval statistics of populations of Chop-S units compare well with changes in the pitch perceived by humans. It is proposed that Chop-S units in the ventral cochlear nucleus may play an important role in pitch extraction: They can convert a purely temporal pitch code as observed in the auditory nerve into a temporal place code of pitch in populations of cochlear-nucleus, Chop-S with different characteristic frequencies, and chopping rates. Thus, populations of cochlear-nucleus Chop-S units, together with their target units presumably located in the inferior colliculus, may serve to establish a stable rate-place code of pitch at the level of the auditory cortex.     

改进庞加莱截面基音检测方法研究

评述了Kubin等人提出的基于庞加莱截面的基音检测方法,指出其存在周期点筛选难、初始点选择方法不完善、要求时域波形具有明显的周期性等问题,导致其实用性不强。在此基础上,提出了变邻域和变初始点的改进算法,并与归一化互相关方法相结合,解决了上述问题。实验结果表明,改进后的基于庞加莱截面的基音检测方法具有时间分辨率高、误判率低、实用性强等优点。     

基于MCKD-EEMD近似熵和TWSVM的齿轮箱故障诊断

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取;为了有效地进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法;首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别;仿真实验表明,采用MCKD-EEMD方法能够有效地提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

仿生鳍条姿态测量及其误差分析

在复杂环境下齿轮箱信号往往会淹没在噪声信号中,特征向量难以提取。为了有效的进行故障诊断,提出了基于最大相关反褶积(MCKD)总体平均经验模态分解(EEMD)近似熵和双子支持向量机(TWSVM)的齿轮箱故障诊断方法。首先采用MCKD方法对强噪声信号进行滤波处理,在采用EEMD方法对齿轮箱信号进行分解,分解后得到本征模函数(IMF)分量进行近似熵求解,得到齿轮特征向量,最后将其输入到TWSVM分类器中进行故障识别。仿真实验表明,采用MCKD-EEMD方法能够有效的提取原始信号,与其他分类器相比,TWSVM的计算时间短,分类效果好等优点。     

Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic Source-Filter Model Based Noise Robust Pitch Estimation

Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency or the strength of excitation amplitude is the main consequence of the disorder. The automatic classification of healthy control and EVT is useful tool for the clinicians. A typical automatic EVT classification involves three steps. The first step is to compute the pitch contour from the speech. The second step is to compute the features from the pitch contour, and the final step is to use a classifier to classify the features into healthy or EVT. It is shown that a high-resolution pitch contour estimated from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC estimation can be very poor in the presence of noise. Hence, a probabilistic source filter model based noise robust GCI detection is used for HPRC estimation. The Empirical mode decomposition based feature extraction is used followed by a support vector machine classifier. The EVT classification performance is evaluated using recordings from 45 subjects. The proposed method is found to perform better than the baseline techniques in eight different additive noise conditions with six SNR levels.     

Fault Diagnosis Method Based on AUPLMD and RTSMWPE for a Reciprocating Compressor Valve

In order to effectively extract the key feature information hidden in the original vibration signal, this paper proposes a fault feature extraction method combining adaptive uniform phase local mean decomposition (AUPLMD) and refined time-shift multiscale weighted permutation entropy (RTSMWPE). The proposed method focuses on two aspects: solving the serious modal aliasing problem of local mean decomposition (LMD) and the dependence of permutation entropy on the length of the original time series. First, by adding a sine wave with a uniform phase as a masking signal, adaptively selecting the amplitude of the added sine wave, the optimal decomposition result is screened by the orthogonality and the signal is reconstructed based on the kurtosis value to remove the signal noise. Secondly, in the RTSMWPE method, the fault feature extraction is realized by considering the signal amplitude information and replacing the traditional coarse-grained multi-scale method with a time-shifted multi-scale method. Finally, the proposed method is applied to the analysis of the experimental data of the reciprocating compressor valve; the analysis results demonstrate the effectiveness of the proposed method.     

一种改进的短时平均幅度差函数算法

在语音信号分析中,对于基音周期的提取目前已有较多的分析和处理方法,在现有的短时平均幅度差函数(AMDF)的处理方法中,只需要加、减和取绝对值运算,运算量较之短时自相关函数大大下降。同时,AMDF函数的谷点提取基音周期比自相关函数的峰值更加尖锐,错判率相对较少,稳健性更高。然而,传统的AMDF算法对窗长的要求较为严格,窗长较短就会有较大的误差。本文针对该缺陷做出的改进算法,使之无论窗长多大均会有较为准确的结果,大大拓展了AMDF算法的适用空间。进一步,将其与同态处理结合,会有更好的效果。     

一种公共网络攻击数据挖掘智能算法研究

公共网络的开放性和自组织特性导致网络容易受到病毒干扰和入侵攻击,对攻击数据的准确高效挖掘能确保网络安全。传统方法采用时频指向性波束特征聚类方法实现攻击数据挖掘,在信噪比较低时攻击数据准确挖掘概率较低。提出一种基于自适应滤波检测和时频特征提取的公共网络攻击数据挖掘智能算法。首先进行公共网络攻击数据的信号拟合和时间序列分析,对含噪的攻击数据拟合信号进行自适应滤波检测,提高信号纯度,对滤波输出数据进行时频特征提取,实现攻击数据的准确挖掘。仿真结果表明,采用该算法进行网络攻击数据挖掘,对攻击数据特征的准确检测性能较高,对干扰的抑制性能较强,能有效实现网络安全防御。     

头部旋转运动下自适应非接触鲁棒性心率检测方法

基于人脸视频的生理信号检测面临的主要挑战是运动伪影噪声.针对受试者头部刚性旋转运动引起的伪影噪声,本文提出利用头部运动信息构建自适应滤波器的非接触式心率检测方法.该方法利用人脸二维和三维的特征点计算受试者运动中头部的偏航和俯仰欧拉角度,并将其作为调控过程噪声协方差的信号质量指数,进而构建了自适应Kalman滤波器,实现了稳健的心率估计.实验结果表明:本文提出的方法可有效抑制头部刚性旋转运动引起的噪声,平均绝对误差为2.22 beat/min,均方根误差为2.76 beat/min,与现有方法相比准确度分别提升9%与24.6%,具有统计显著性.本文提出的头部旋转角度自适应非接触鲁棒性心率检测方法在自发运动的真实场景下能有效提升检测的准确性,扩大了成像式光电容积描记技术在视频健康监测领域的使用场景.     

Chatter detection in band sawing based on discriminant analysis of sound features

In the paper chatter detection in band sawing is considered as a signal processing and classification problem. A multi-sensory experimental setup was established on an industrial band saw including sound, acceleration and cutting force, and measurements. Based on an experimental analysis sound signal is shown to be the most appropriate for chatter detection, therefore a sound-based online chatter detection method is proposed. The method consists of a sound signal pre-processing with Short-Time Fourier Transform, extraction of features in frequency space with optimal threshold and application of Quadratic Discriminant Analysis for chatter detection. The proposed method tested with twofold cross validation yields over 96% success of chatter detection.