基于MFCC特征提取和改进SVM的语音情感数据挖掘分类识别方法研究

设计一种语音情感数据挖掘分类识别方法。对语音情感信号进行预处理,进一步从语音话语中提取Mel频率倒谱系数(MFCC)和Mel能谱动态系数(MEDC);使用支持向量机(SVM)来分类不同的情绪状态,如愤怒、快乐、悲伤、中立、恐惧等,并基于径向基函数(RBF)内核进行训练阶段;应用柏林情感数据库和CASIA汉语情感语料库从情绪语音文件中提取特征。实验结果表明,柏林数据库和CASIA汉语情感语料库的正确识别率分别为82%和90.39%。与几种先进的对比方法进行比较,该方法在不同降维、不同信噪比下均取得了最优的识别精度。     

有效的基于内容的音频特征提取方法

音频特征提取是音频分类的基础;好的特征将会有效提高分类精度。在提取频域特征Mel频率倒谱系数（MFCC）的同时;对每一帧信号做离散小波变换;提取小波域特征;把频域和小波域特征相结合计算其统计特征。通过SVM模型建立音频模板;对纯语音、音乐及带背景音乐的语音进行分类识别;取得了较高的识别精度。     

基于样本熵与MFCC融合的语音情感识别

提出一种基于样本熵与Mel频率倒谱系数(MFCC)融合的语音情感识别方法。利用支持向量机分别对样本熵统计量与MFCC进行处理,计算其属于高兴、生气、厌烦和恐惧4种情感的概率,采用加法规则和乘法规则对情感概率进行融合,得到识别结果。仿真实验结果表明,该方法的识别率较高。     

基于EMD的改进MFCC的语音情感识别

人在不同情感下的语音信号其非平稳性尤为明显,传统的MFCC只能反映语音信号的静态特征,经验模态分解能够精细地刻画语音信号的非平稳特性。为提取情感语音的非平稳特征,用经验模态分解将情感语音信号分解为一系列固有模态函数分量,通过Mel滤波器后取其对数能量,进行DCT反变换后得到改进的MFCC作为情感识别的新特征,采用支持向量机对高兴、生气、厌烦和恐惧等四种语音情感识别。仿真实验结果表明：改进的MFCC识别率达到77.17%,在不同的信噪比下,识别率最大可提高3.26%。     

一种基于MFCC的音频数据检索方法

以LPC倒谱系数(LPCC)为特征参数的音频数据检索是人们常用的一种方法,但这种方法没有充分考虑到人耳听觉的非线性特性.因此,提出了一种采用MFCC及其差分倒谱系数相结合作为特征参数来做音频数据检索的方法.通过实验对比分析结果表明,这种音频数据检索方法比LPCC的检索具有更高的识别率.     

基于Bark子波变换的MFCC特征提取

为提高低信噪比环境下语音的鲁棒性,提出一种改进的Mel频率倒谱系数(MFCC)特征提取方法。在传统MFCC特征提取的基础上,引入更适应人耳听觉系统的Bark子波变换,在快速傅里叶变换之前对语音进行预处理,并在MFCC提取方法中代替离散余弦变换;在语音预处理阶段,利用改进的Lanczos窗函数抑制旁瓣以提高语音鲁棒性。实验表明,与传统MFCC方法相比,在噪声环境下,改进方法具有更高的说话人识别率。     

基于ARIMA预测MFCC特征的声纹同一性鉴定方法

声纹识别技术的关键是从语音信号中提取具有说话人特征的语音特征参数.考虑到当下大多是运用鉴定人的经验对两段语音是否来源于同一人进行判定,在前期研究的基础上,结合MFCC特征,提出一种基于ARIMA预测的声纹同一性鉴定方法,以提高具有年份差距的检材与样本比对的准确率.此方法在Mel倒谱系数声纹同一性鉴定方法基础上,采用自回...     

基于MFCC和加权动态特征组合的环境音分类

提出了基于Mel倒谱系数和加权的一阶、二阶差分Mel倒谱系数特征参数组合的环境音分类,实验结果表明以MFCC＋α△MFCC＋β△△MFCC为特征参数的分类正确率明显高于MFCC、MFCC＋△MFCC和MFCC＋△MFCC＋△MFCC。     

一种静态特征与动态特征结合的方言辨识方法

针对MFCC仅反映语音静态特征导致的方言识别率低的问题,而SDC由于考虑了前后帧差分倒谱的影响,能反映语音的动态特征;同时考虑方言的静态与动态特征,对普通话、上海话、广东话和闽南话4种方言进行MFCC特征和SDC特征提取,将其两组特征组合送入支持向量机进行辨识,并研究了针对4种方言的SDC的局部最优参数组合。仿真实验结果表明,同时考虑方言的静态与动态特征方法的识别率高达92.5%,但识别率的提高是以延长运算时间为代价的。     

基于MFCC特征的声纹同一性鉴定方法

声纹作为当代司法鉴定技术发展的产物,在现代声像资料鉴定中发挥了至关重要的作用.传统的声纹分析方法是基于声音处理工具进行手工分析的,考虑到其具有严格的文本相关性以及比对的臆断性的缺点,其作为证据鉴定意见的证明力有待加强.文中提出了一种基于Mel频率倒谱系数的同一性鉴定方法,即提取并量化包含原始声音的共振峰及其时间轴信息的包络作为声纹特征进行同一性比对.此方法改进了传统Mel频率倒谱系数的不足,提取共振峰的突变并将元音与响辅音的转变特性加入声纹特征,以提高其识别度.实验证明,此方法在检材与样本无关的情况下,同一性鉴定的准确率达到了85％,方差控制在9％左右,具有良好的同一性识别;而在非同一性鉴定中,该方法也能在结合人工分析的情况下给出较准确的结果.     

语音MFCC特征提取的FPGA实现

提出了在FPGA上实现语音MFCC特征提取的方法,巧妙设计了FFT、三角滤波、取对数和DCT的硬件结构,采hl多时钟、状态机、模块复用、IP核和多级流水技术,大大提高了运算速度,实验表明与软件相比,结果误差在l%以内,50M时钟频率下每秒语音数据计算时间仅为8.5 ms,与VQ、HMM结合后,对系统识别率影响可以忽略,达到了嵌入式实时系统的应用要求.     

基于知识图谱嵌入和补全的电梯故障预测

为及早预测电梯发生的常见故障,提高电梯设备的维保质量和效率,提出基于规则推理、知识图谱嵌入技术和知识图谱补全技术实现电梯故障预测的方法,在构建电梯故障知识图谱后,通过改进的组合模型将三元组中的实体和关系训练为连续的低维向量空间,实现三元组对于故障预测相关运算的兼容,通过组合模型实现电梯实体、关系和故障实体三元组的预测....     

基于MFCC与分形维数混合参数的语音识别

针对语音气流中具有混沌特征,而分形可以定量地分析混沌现象,故分形可用来分析语音信号。语音波形具有分形特征,将分形用于改善语音识别技术更好地表现语音的特征,避免传统的分段线性处理所产生的局限性。将传统特征参数MFCC与分形特征结合起来,组成混合参数用于语音识别。实验结论显示,基于MFCC与分形维数混合参数的语音识别方法要好于使用单一MFCC参数的语音识别方法。     

A hybrid financial analysis model for business failure prediction

Accounting frauds have continuously happened all over the world. This leads to the need of predicting business failures. Statistical methods and machine learning techniques have been widely used to deal with this issue. In general, financial ratios are one of the main inputs to develop the prediction models. This paper presents a hybrid financial analysis model including static and trend analysis models to construct and train a back-propagation neural network (BPN) model. Further, the experiments employ four datasets of Taiwan enterprises which support that the proposed model not only provides a high predication rate but also outperforms other models including discriminant analysis, decision trees, and the back-propagation neural network alone.     

Comparison of different implementations of MFCC

The performance of the Mel-Frequency Cepstrum Coefficients (MFCC)may be affected by (1)the number of filters,(2)the shape of filters,(3)the way in which filters are spaced,and (4)the way in which the power spectrum is warped.In this paper,several comparison experiments are done to find a best implementation.The traditional MFCC calculation escludes the 0th coefficient for the reason that it is regarded as somewhat unrelable.According to the analysis and experiments.the authors find that it can be regarded as the generalized frequency band energy(FBE)and is hence useful,which results in the FBE-MFCC.The authors also propose a better analysis,namely the auto-regressive analysis.on the frame energy,which outperforms its 1st and /or 2nd order differential derivatives.Experiments with the “863“ Speech Database show that,compared with the traditional MFCC with its corresponding autoregressive analysis coefficinets.the FBE-MFCC and the frame energy with their corresponding auto-regressive analysis coefficients form the best combination,reducing the Chinese syllable error rate(CSER)by about 10%,while the FBE-MFCC with the corresponding auto-regressive analysis coefficients reduces CSER by 2.5%,Comparison experiments are also done with a quite casual Chinese speech database,named Chinese Annotated Spontaneous Speech(CASS) corpus.The FBE-MFCC can reduce the error rate by about 2.9% on an average.     

基于视觉特征的BBS用户发言信息抽取

针对传统从BBS主题网页中自动抽取用户发言信息的问题,提出独立于网页脚本语言的自动抽取方法,可根据网页提供的视觉信息进行有效抽取.通过归纳BBS主题网页内部用户发言信息的视觉特征,并以此为依据指导整个抽取过程.实验结果表明,基于视觉的抽取方法可以得到较高的抽取质量.     

基于灰色预测的分布式系统动态故障检测服务

针对已有故障检测服务不能有效满足分布式系统需求问题,设计了一种适用于分布式系统的动态故障检测服务.根据分布式系统的特点,在定义分布式系统模型的基础上,提出了动态故障检测服务架构.结合心跳策略和灰色预测方法,设计了一种动态心跳机制,并给出了预测模型和动态预测策略,提出了基于该动态心跳机制的分布式系统的故障检测算法.最后,仿真实验验证了该算法的正确性和有效性.     

一种基于累加PSO-SVM的网络安全态势预测模型

在网络安全态势感知系统中,态势预测是关键的环节.为了保证及提高态势预测的精度,结合粒子群算法的寻优性能好和支持向量机的预测准确的优势,提出了一种在数据累加预处理基础上的PSO-SVM预测模型.此模型利用将原始序列累加,弱化了原始序列中的不规则扰动影响,增强了序列的规律性的特点,与粒子群优化支持向量机(PSO-SW)相结合,更好地发挥预测精度高的优势,更能保证预测精度.通过仿真实验检验此模型的有效性,并与PSO-SVM预测模型的结果进行对比,验证了其预测精度的优越性.     

A boosting approach for corporate failure prediction

Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of this problem, until now only classical machine learning tools have been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we introduce novel discerning measures to rank independent variables in a generic classification task. On the other hand, we apply boosting techniques to improve the accuracy of a classification tree. We apply this methodology to a set of European firms, considering the usual predicting variables such as financial ratios, as well as including novel variables rarely used before in corporate failure prediction, such as firm size, activity and legal structure. We show that our approach decreases the generalization error about thirty percent with respect to the error produced with a classification tree. In addition, the most important ratios deal with profitability and indebtedness, as is usual in failure prediction studies. E. A. Cortés · M. G. Martínez · N. G. Rubio. The authors teach Statistics at the Faculty of Economic and Business Sciences in the University of Castilla-La Mancha. Esteban Alfaro completed his degree in Business in 1999 and got his Ph.D. in Economics in 2005, both in the University of Castilla-La Mancha. His thesis dealt with the application of ensemble classifiers to corporate failure prediction. Matías Gámez got his degree in Mathematics at the University of Granada in 1991 and finished a Master in Applied Statistics a year after. He completed his Ph.D. in Economics at the University of Castilla-La Mancha in 1998 on the application of geo-statistical techniques to the estimation of housing prices. Noelia García got her degree in Economics at the University of Madrid (UAM) in 1996 and completed her Ph.D. in Economics in 2004 on the construction of an intelligent and automated system for property valuation through the combination of neural nets and a geographic information system (GIS). Current research deals with spatial statistics and the combination of classifiers (decision trees and neural nets) for solving heated topics in the Economics.     

基于改进KCCA的快速特征提取方法

KCCA特征提取技术具有处理非线性数据的良好性能,但是存在计算量大、特征提取缓慢的局限性.针对KCCA的这一缺点,在研究KCCA特征提取技术和SVDD分类理论的基础上,提出了一种基于改进KCCA的快速特征提取方法,并将改进后的KCCA与SVDD的优势相结合应用于人脸识别中.通过在ORL人脸库上的实验仿真和对比结果验证了所提出方法的有效性.