心音身份识别技术的研究

文中研究心音身份识别的基本原理和实现方法.首先分析心音信号的特性和作为生物识别的可行性;然后建立基于心音子波族的心音信号合成模型,并且用特征向量分布相图形象地比较两个心音的特征,用倒谱减法消除听诊器的类型和位置变化所产生的影响;最后,采用心音线性频带倒谱(HS-LBFC)提取心音特征参数,用相似距离等实现心音的身份识别.为了突出心音在时、频域上存在的差异,重点研究了构建心音子波的方法,合成模型中各参数的计算方法,以及心音特征参数的确定和对应的数据处理技术.实际实验结果表明,该方法具有很好的识别率和实用性.     

基于深度卷积神经网络的心音分类算法

针对现有心音分类算法普适性差、依赖于对基本心音的精确分割、分类模型结构单一等问题,提出采用大量未经过精确分割的心音二维特征图训练深度卷积神经网络(CNN)的方法;首先采用滑动窗口方法和梅尔频率系数对心音信号进行预处理,得到大量未经过精确分割的心音特征图;然后利用深度CNN模型对心音特征图进行训练和测试;根据卷积层间连接方式的不同,设计了 3种深度CNN模型:基于单一连接的卷积神经网络、基于跳跃连接的卷积神经网络、基于密集连接的卷积神经网络;实验结果表明,基于密集连接的卷积神经网络比其他两种网络具备更大的潜力;与其他心音分类算法相比,该算法不依赖于对基本心音的精确分割,且在分类准确率、敏感性和特异性方面均有提升.     

一种新的低俗语音识别方法

为了应对低俗视频语音等多媒体信息在网络上的大量传播,提出了一种基于移位差分倒谱参数特征的低俗语音识别方法。该方法对输入的语音信号进行分帧,提取移位差分倒谱参数特征,采用了高斯混合模型进行粗分类,对粗分为低俗的语音帧再用支持向量机分类器进行确认。实验结果表明,该方法具有较高的正识别率和较低的误识别率,可用于网络上低俗语音和视频信息的过滤。     

基于声音特征优化和改进支持向量机的鸟声识别

为了在低参数量下提高鸟鸣声的识别准确率,提出了一种新的鸟声识别方法,包括鸟声信号特征优化和乌鸦搜索-支持向量机（Support Vector Machine,SVM）分类识别。该方法首先采用主成分分析法对从鸟声中提取的梅尔频率倒谱系数（Mel Frequency Cepstrum Coefficient,MFCC）和翻转梅尔频率倒谱系数进行选择,得到优化后的声音特征参数并将其作为鸟声识别算法的输入;然后利用乌鸦搜索算法对SVM的核参数和损失值进行选优,得到改进的SVM网络用于鸟声分类识别。试验结果表明,该方法对5种鸟声识别的准确率为92.2%,声音特征维数在16时可以得到最好的识别效果。该方法为野外鸟声自动识别提供了一种可行的方式。     

基于HMM和WNN的心音信号身份识别研究*

将隐马尔可夫模型（HMM）与小波神经网络（WNN）相结合,提出了一种基于心音信号的身份识别方法。该方法首先利用HMM对心音信号进行时序建模,并计算出待识别心音信号的输出概率评分;再将此识别概率评分作为小波神经网络的输入,通过小波神经网络将HMM的识别概率值进行非线性映射,获取分类识别信息;最后根据混合模型的识别算法得出识别结果。实验采集80名志愿者的160段心音信号对所提出的方法进行验证,并与GMM模型的识别结果进行了对比,结果表明,所选方法能够有效提高系统的识别性能,达到了比较理想的识别效果。     

港口扩建的物流规划模型及其计算机仿真*

随着经济的发展,国内外贸易迅速增长,港口航运业务也日趋繁忙,港口拥堵的现象常有发生,以致许多船舶不得不把时间浪费在等待服务上。为了减少船舶的等待时间,在原来的港口上进行扩建是十分必要的,为此提出了港口扩建的资源配置协调优化模型。提出在原有港口资源的基础上增加泊位数和提高服务率两种方案,并针对以上两种方案分别构建了所需总费用的数学模型;运用排队论方法和计算机仿真优化方法进行求解,通过一个实例来说明计算机仿真技术在港口扩建的资源协调优化的适用性和有效性。     

IMF复杂度特征在心音信号分类识别中的应用

为提高非线性、非平稳心音信号特征提取的准确性和分类识别的高效性,提出一种基于固有模态函数（Intrinsic Mode Function,IMF）复杂度和二叉树支持向量机（Binary Tree Support Vector Machine,BT-SVM）的心音分类识别方法。对心音进行经验模式分解（Empirical Mode Decomposition,EMD）,得到若干反映心音本体特征的平稳IMF分量;利用互相关系数准则对其筛选,计算所选IMF分量的复杂度值为信号的特征;将其组成特征向量输入到BT-SVM进行分类识别。临床数据仿真结果表明,该方法能有效提取心音特征,与传统识别方法相比,具有训练时间短,识别率高等优点。     

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier–Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.     

抗环境音干扰的设备声音故障监测方法

变压器等大型设备在运行过程中发声具有辨识性和平稳性的特点，但容易受各种环境音的干扰，针对该问题，本文利用声音信号处理、特征提取、模式匹配等技术，提出了一种抗多种环境音干扰的设备声音故障监测方案.首先对在各种环境声音中变压器的正常和故障声音进行采集和预处理，然后对其提取出MFCC特征并降维，对变压器正常工作声音特征通过OPTICS算法进行训练，得到一个具有多个分类的标准集，最后将标准集与包含故障声音的测试样本进行匹配，若出现不匹配情况但经人工检验为误报，则将其归为新的分类.实验结果表明：该方法不仅能很好的识别样本，也能在新的正常声音出现时通过标准集增强模块来优化标准集，从而提高识别准确率并降低误警率.     

Intelligent methodologies for cardiac sound signals analysis and characterization in cepstrum and time-scale domains

Biometric authentication is the process that allows an individual to be identified based on a set of unique biological features data. In this study, we present different experiments to use the cardiac sound signals (phonocardiogram “PCG”) as a biometric authentication trait. We have applied different features extraction approaches and different classification techniques to use the PCG as a biometric trait. Through all experiments, data acquisition is based on collecting the cardiac sounds from HSCT-11 and PASCAL CHSC2011 datasets, while preprocessing is concerned with de-noising of cardiac sounds using multiresolution-decomposition and multiresolution-reconstruction (MDR-MRR). The de-noised signal is then segmented based on frame-windowing and Shanon energy (SE) methods. For feature extraction, Cepstral (Cp) domain (based on mel-frequency) and time-scale (T-S) domain (based on Wavelet Transform) features are extracted from the de-noised signal after segmentation. The features, extracted from the Cp-domain and the T-S domain, are fed to four different classifiers: Artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and K-nearest neighbor (KNN). The performance of the classifications is assessed based on the k-fold cross validation. The computation complexity of the feature extraction domains is expressed using the Big-O measurements. The T-S features are superior to PCG heart signals in terms of the classification accuracy. The experiments' results give the highest classification accuracy with lowest computation complexity for RF in the Cp domain and SVM and ANN in the T-S domain.     

基于支持向量机的心音信号自动识别

心音信号识别对心血管疾病的诊断具有重要意义,为了提高心音信号的识别性能,提出一种基于支持向量机的心音信号自动识别方法。首先采用小波分析对心音信号进行降噪预处理,然后提取心音信号的Mel频率倒谱系数作为心音信号特征,最后采用支持向量机建立心音信号分类器,对采集心音信号数据的识别性能进行验证。实验结果表明,本文方法的心音信号平均识别率高达93%以上,可以准确识别正常和各种异常的心音信号。      

指横纹:一种新的生物身份特征

本文提出一种新的基于指横纹的生物特征认证方法. 指横纹图像具有抗噪性强、纹路简单、可分性强的优点, 并易于与其它手部特征如手形、掌纹等特征形成融合认证系统. 本文采用基于 Gabor 滤波的方法提取指横纹特征点, 并基于互相关点匹配与决策级分数融合完成在线认证系统. 评估系统建立在包含 98 个人、1971 幅图像的数据库上, 平均错误率仅为 0.57\%, 验证了指横纹作为一种生物特征的可靠性与可行性, 同时也证实了认证方法的有效性.     

Automatic phonocardiograph signal analysis for detecting heart valve disorders

Skilled cardiologists probe heart sounds by electronic stethoscope through human ears, but interpretations of heart sounds is a very special skill which is quite difficult to teach in a structured way. Because of this reason, automatic heart sound analysis in computer systems would be very helpful for medical staffs. This paper presents a complete heart sound analysis system covering from the segmentation of beat cycles to the final determination of heart conditions. The process of heart beat cycle segmentation includes autocorrelation for predicting the cycle time of a heart beat. The feature extraction pipeline includes stages of the short-time Fourier transform, the discrete cosine transform, and the adaptive feature selection. Many features are extracted, but only a few specific ones are selected for the classification of each hyperplane based on a systematic approach. The experiments are done by a public heart sound database released by Texas Heart Institute. A very promising recognition rate has been achieved.     

智能监控前端系统中异常声音检测的实现

针对智能监控前端系统中异常声音检测的高实时性和高准确率要求,提出一种基于混合特征参数和改进动态时间弯折(DTW)算法的异常声音检测方案。通过短时幅度和过动态门限率判决声音端点,提取包括短时幅度、美尔倒谱系数和差分系数在内的混合特征参数,采用改进的DTW算法进行声音识别。在TI TMS320DM368处理器平台上的实验结果表明,基于该方案的智能监控前端系统对异常声音的识别时间小于1 s,准确率达到89.3%。     

Towards heart sound classification without segmentation via autocorrelation feature and diffusion maps

Heart sound classification, used for the automatic heart sound auscultation and cardiac monitoring, plays an important role in primary health center and home care. However, one of the most difficult problems for the task of heart sound classification is the heart sound segmentation, especially for classifying a wide range of heart sounds accompanied with murmurs and other artificial noise in the real world. In this study, we present a novel framework for heart sound classification without segmentation based on the autocorrelation feature and diffusion maps, which can provide a primary diagnosis in the primary health center and home care. In the proposed framework, the autocorrelation features are first extracted from the sub-band envelopes calculated from the sub-band coefficients of the heart signal with the discrete wavelet decomposition (DWT). Then, the autocorrelation features are fused to obtain the unified feature representation with diffusion maps. Finally, the unified feature is input into the Support Vector Machines (SVM) classifier to perform the task of heart sound classification. Moreover, the proposed framework is evaluated on two public datasets published in the PASCAL Classifying Heart Sounds Challenge. The experimental results show outstanding performance of the proposed method, compared with the baselines.     

Analysis and modeling of acoustic information for automatic dialect classification

A primary challenge in the field of automatic speech recognition is to understand and create acoustic models to represent individual differences in their spoken language. Individual’s age, gender; their speaking styles influenced by their dialect may be few of the reasons for these differences. This work investigates the dialectal differences by measuring the analysis of variance of acoustic features such as, formant frequencies, pitch, pitch slope, duration and intensity for vowel sounds. This paper attempts to discuss methods to capture dialect specific knowledge through vocal tract and prosody information extracted from speech that can be utilized for automatic identification of dialects. Kernel based support vector machine is utilized for measuring the dialect discriminating ability of acoustic features. For the spectral feature shifted delta cepstral coefficients along with Mel frequency cepstral coefficients gives a recognition performance of 66.97 %. Combination of prosodic features performs better with a classification score of 74 %. The model is further evaluated for the combination of spectral and prosodic feature set and achieves a classification accuracy of 88.77 %. The proposed model is compared with the human perception of dialects. The overall work is based on four dialects of Hindi; one of the world’s major languages.     

高压水射流靶物反射声信号的特征提取方法

为了提高利用高压水射流靶物反射声信号识别靶物材质的效率,针对地雷探测过程常见的地雷、石块、砖块和木块4种靶物,采用不同的特征提取方法来识别靶物材质。在分析Mel频率倒谱系数及小波包变换倒谱系数基本原理的基础上,结合靶物反射声信号的特点,提出了一种基于Mel频率倒谱和小波包变换倒谱特征融合的特征提取方法:利用小波包变换将原始靶物反射声信号划分为若干子频段,选取其中一个子频段作为低频和高频的划分层;低频部分提取Mel频率倒谱系数作为特征值,高频部分则提取小波包变换倒谱系数作为特征值,将2组特征值线性合并为一组新的特征向量,用于靶物材质的识别。采用最小二乘支持向量机建立多分类模型,验证基于单一特征和基于特征融合的特征提取方法的识别率。实验结果表明,在取得低频与高频的最佳划分层时,基于特征融合的特征提取方法的平均识别率达到82.812 5%,较单一的利用Mel频率倒谱系数或小波包变换倒谱系数作为特征向量时的平均识别率分别提高了10.312 5%和7.812 5%。     

Probability Enhanced Entropy (PEE) Novel Feature for Improved Bird Sound Classification

Identification of bird species from their sounds has become an important area in biodiversity-related research due to the relative ease of capturing bird sounds in the commonly challenging habitat. Audio features have a massive impact on the classification task since they are the fundamental elements used to differentiate classes. As such, the extraction of informative properties of the data is a crucial stage of any classification-based application. Therefore, it is vital to identify the most significant feature to represent the actual bird sounds. In this paper, we propose a novel feature that can advance classification accuracy with modified features, which are most suitable for classifying birds from its audio sounds. Modified Gammatone frequency cepstral coefficient (GTCC) features have been extracted with their frequency banks adjusted to suit bird sounds. The features are then used to train and test a support vector machine (SVM) classifier. It has been shown that the modified GTCC features are able to give 86% accuracy with twenty Bornean birds. Furthermore, in this paper, we are proposing a novel probability enhanced entropy (PEE) feature, which, when combined with the modified GTCC features, is able to improve accuracy further to 89.5%. These results are significant as the relatively low-resource intensive SVM with the proposed modified GTCC, and the proposed novel PEE feature can be implemented in a real-time system to assist researchers, scientists, conservationists, and even eco-tourists in identifying bird species in the dense forest.

     

Identification of the normal and abnormal heart sounds using wavelet-time entropy features based on OMS-WPD

In this paper, a novel method was put forward for automatic identification of the normal and abnormal heart sounds. After the original heart sound signal was pre-processed, it was analyzed by the optimum multi-scale wavelet packet decomposition (OMS-WPD), and then the wavelet-time entropy was applied to extract features from the decomposition components. The extracted features were then applied to a support vector machine (SVM) for identification of the normal and five types of abnormal heart sounds. To show the robustness of the proposed method, its performance was compared with four other popular heart sound processing methods. Extensive experimental results showed that the feature extraction method proposed in this paper has convincing identification results, which could be used as a basis for further analysis of heart sound.     

手机C/S模式心音特征提取与波形显示系统

针对现行心音信号特征提取模式下存在的局限性,结合手机移动通讯技术的发展,设计了一种基于手机客户端和服务器模式的心音信号分析诊断系统.利用Java Me语言在手机上进行心音信号可视化编程处理,使在手机上显示信号时域图谱,并通过移动互联网将采样信号传输到服务器端进行小波分解,提取心音信号特征值,最后在手机上返回信号的异常结果同时存储在数据库.通过实验结果表明了该方案的可行性,实现了心音信号的自动诊断,对于诊断心脏突发疾病和慢性病具有显著的效果.