Speech Endpoint Detection in Noisy Environments Using EMD and Teager Energy Operator

Accurate endpoint detection is a necessary capability for speech recognition. A new energy measure method based on the empirical mode decomposition （EMD） algorithm and Teager energy operator （TEO） is proposed to locate endpoint intervals of a speech signal embedded in noise. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions （IMFs）, which is a zero-mean AM-FM component. Then TEO can be used to extract the desired feature of the modulation energy for IMF components. In order to show the effectiveness of the proposed method, examples are presented to show that the new measure is more effective than traditional measures. The present experimental results show that the measure can be used to improve the performance of endpoint detection algorithms and the accuracy of this algorithm is quite satisfactory and acceptable.

Adaptive Endpoint Detection Based on Subband Speech

An adaptive endpoint detection algorithm based on band energy and adaptive smoothing algorithm is described. This algorithm utilizes the capability of adaptive smoothing algorithm that intensifies the discontinuity between local areas. The band energy features are selected because of their usefulness in detecting high energy regions (in the incoming signal) and making the distinction between speech and noise. Heuristic 'edge-focusing' is used to endpoint detection to save the time in iteration.

A fuzzy adaptive smoothing approach to robust endpoint detection based on MDL using sub-band speech

To develop a more robust endpoint detection algorithm, this paper first proposes a fuzzy adaptive smoothing algorithm. The general idea underlying adaptive smoothing is to adapt the short-term sub-band mean of the amplitude to the local attributes of speech on the basis of discontinuity measures. The adaptive smoothing algorithm in this paper utilizes a scale-space framework through the minimal description length （MDL）. We recommend using the fuzzy muhi-attribute decision making approach to select the proper sub-bands where the word boundary can be more reliably detected. The process and simulation of the fuzzy adaptive smoothing algorithm are given. The parameters utilize the mean amplitude of the audible frequency range （300 -3 700 Hz） and the sub-band mean of the amplitude （16 band filter-bank）. We selected the audible band energy because of its usefulness in detecting high-energy regions and making the distinction between speech and noise. Otherwise, the fuzzy adaptive smoothing algorithm is processed in sub-band speech to utilize the full range of frequency information.

Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion Detection Systems

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection methods.Signature-based intrusion detection methods can be used to detect attacks;however,they cannot detect new malware.Endpoint detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques.However,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory efficiency.Machine learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack type.To overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature selection.The proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack type.The experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 features.Furthermore,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

A recursive calculating algorithm for higher-order cumulants over sliding window and its application in speech endpoint detection

Regarding the performance of traditional endpoint detection algorithms degrades as the environment noise level increases, a recursive calculating algorithm for higher-order cu- mulants over a sliding window is proposed. Then it is applied to the speech endpoint detection. Furthermore, endpoint detection is carried out with the feature of energy. Experimental results show that both the computational efficiency and the robustness against noise of the proposed algorithm are improved remarkably compared with traditional algorithm. The average prob- ability of correct point detection （Pc-point） of the proposed voice activity detection （VAD） is 6.07% higher than that of G.729b VAD in different noisy at different signal-noise ratios （SNRs） environments.

Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter

The Perception Spectrogram Structure Boundary（PSSB）parameter is proposed for speech endpoint detection as a preprocess of speech or speaker recognition.At first a hearing perception speech enhancement is carried out.Then the two-dimensional enhancement is performed upon the sound spectrogram according to the difference between the determinacy distribution characteristic of speech and the random distribution characteristic of noise.Finally a decision for endpoint was made by the PSSB parameter.Experimental results show that,in a low SNR environment from-10 dB to 10 dB,the algorithm proposed in this paper may achieve higher accuracy than the extant endpoint detection algorithms.The detection accuracy of 75.2%can be reached even in the extremely low SNR at-10 dB.Therefore it is suitable for speech endpoint detection in low-SNRs environment.

A signal processing method for the friction-based endpoint detection system of a CMP process

A signal processing method for the friction-based endpoint detection system of a chemical mechanical polishing （CMP） process is presented. The signal process method uses the wavelet threshold denoising method to reduce the noise contained in the measured original signal, extracts the Kalman filter innovation from the denoised signal as the feature signal, and judges the CMP endpoint based on the feature of the Kalman filter innovation sequence during the CMP process. Applying the signal processing method, the endpoint detection experiments of the Cu CMP process were carried out. The results show that the signal processing method can judge the endpoint of the Cu CMP process.

ENDPOINT DETECTOR OF NOISY SPEECH SIGNAL USING A RECURRENT NEURAL NETWORK

ＩｎｔｒｏｄｕｃｔｉｏｎＥｎｄｐｏｉｎｔｄｅｔｅｃｔｉｏｎｏｆｓｐｅｅｃｈｓｉｇｎａｌｉｓｉｍｐｏｒｔａｎｔｉｎｍａｎｙａｒｅａｓｏｆｓｐｅｅｃｈｐｒｏｃｅｓｓｉｎｇｔｅｃｈｎｏｌｏｇｙ，ｓｕｃｈａｓｓｐｅｅｃｈｅｎｈａｎｃｅｍｅｎｔ，ｓｐｅｅｃｈｒ...

Linux终端检测响应系统的文件防护绕过技术研究

目前,国内外很多厂商推出了Linux系统中的终端检测响应(Endpoint Detection and Response,EDR)系统,为云平台、物联网、大数据计算等基础设施提供全面的安全检测和防护服务。但是,针对EDR文件防护功能的绕过攻击能够帮助恶意行为规避监控,造成严重的系统和数据安全风险。针对开源和商业闭源的Linux EDR系统,首先,阐述了文件防护功能的底层实现机制,对其核心技术原理进行了分析;其次,重点梳理了4种现有公开的文件防护绕过技术,提出了3种尚未公开的绕过技术,并且总结提炼为3种攻击类型;再次,基于上述绕过技术编写了验证工具,通过测试证明了这些技术方法对于部分Linux EDR系统的文件防护绕过能力;最后,给出了相应的安全防护建议。

一种基于能零积的改进端点检测算法

针对基于短时能量和短时过零率的传统端点检测算法在信噪比低于10 dB时其准确性明显下降的问题,提出一种基于声谱图特征增强的能零积端点检测改进算法。首先,对音频信号进行谱减法降噪,提升信号的信噪比;其次,依次对声谱图进行腐蚀、二值化和膨胀处理,以实现声谱图特征增强;最后,提取能零积特征,并利用双阈值端点检测算法对音频信号进行端点检测。实验结果表明,该算法在不同信噪比条件下可以有效捕获有环境异常音片段,具有良好的鲁棒性。

非平稳强噪声环境中的音频信号端点检测系统

为提高音频信号端点识别能力,设计一种非平稳强噪声环境中的音频信号端点检测系统。构建音频信号端点检测硬件单元,利用预处理单元对音频信号进行预加重、分帧以及加窗处理后,端点检测单元在提取处理音频信号的MFCC倒谱距离特征、频带方差特征的基础上,依据动态阈值估计策略确定恰当阈值;通过双特征参数双门限法来实现对音频信号起止点的确定以及语音帧和非语音帧的分离;利用包络确定延时单元,防止噪声段被错误识别为语音段,避免出现拖尾太长问题。实验结果表明,所设计系统可实现非平稳强噪声环境音频信号端点检测,检测误差满足设定要求。

基因多态性检测在高危缺血性脑卒中患者抗血小板药物二级预防中的指导价值

目的观察基因多态性检测在高危缺血性脑卒中患者抗血小板药物二级预防中的指导价值。方法选取2017年10月—2018年10月于福州市第二医院住院的高危缺血性脑卒中患者200例,采用随机数字表法分为对照组和试验组,各100例。对照组根据指南推荐选用抗血小板药物治疗,试验组根据基因检测分析结果及指南推荐选择抗血小板药物治疗。比较2组随访3、6、12个月终点事件发生率,存活时间,随访12个月累积生存率。结果试验组随访3、6、12个月终点事件发生率均低于对照组(P<0.01)。试验组平均存活时间长于对照组(P<0.01)。生存曲线结果显示,试验组12个月累积生存率高于对照组(χ^(2)=7.650,P<0.01)。结论通过基因多态性检测选用抗血小板药物治疗高危缺血性脑卒中患者能明显降低其12个月内终点事件发生率,提高患者累积生存率,该检测有利于指导临床医师个体化精准用药。

基于改进变分模态分解的供水管道单测点泄漏检测定位方法

为克服传统负压波技术用于供水管道泄漏检测定位时,只能对2个传感器之间的管段进行检测与定位的不足,将压缩波和流体波引入检测和定位过程中,同时为了提升漏点定位精度,提出一种基于变分模态分解和Teager能量算子相结合的泄漏初始时刻计算方法。该方法首先采用VMD将被测信号分解为多个IMF,再将各个IMF和参考信号进行互相关处理和均方差计算,建立目标函数;然后通过设定阈值剔除大部分噪声,并对经过初步去噪后的信号所对应的模态分量进行重构,最终实现信号降噪。为验证该方法的有效性,对不同材质的管道进行泄漏工况模拟仿真,其结果表明:该方法对泄漏初始时刻计算误差较小,漏点定位精度较高;对PVC管泄漏定位的相对误差为0.49%,对钢管泄漏定位的相对误差为0.14%。

基于语音特征与MFCC谱图融合模型的抑郁症检测

据《2022年国民抑郁症蓝皮书》显示,我国的抑郁症患者人数接近1亿。为更好地实现抑郁症辅助检测,文章首先利用MFPH端点检测方法分离了语音信号的有声段、无声段,其次提取了停顿时长、停顿次数、短时过零率等语音特征及MFCC特征谱图。对比分析发现,基于语音特征与MFCC特征谱图的融合模型在测试集上的准确率可以达到76.4%。

民族语言的语音识别研究

民族语言语音识别的研究内容主要涵盖连续语音识别、孤立词识别以及语音端点检测等方面。文章旨在挖掘和总结有关藏族、苗族和蒙古族语音识别的文献,分析这三种民族语言语音识别研究所面临的主要难点和研究趋势。

基于梅尔频率倒谱系数的语音清晰度DRT识别

语音清晰度在通信终端、设备系统语音识别方面具有重要意义。本文对110dB噪声干扰下采集到的语音信号进行谱减法降噪,双门限端点检测提取发音字段,然后提取梅尔频率倒谱系数(MFCC),再将其进行差分计算,得到一阶和二阶分量,结合短时能量作为语音信号的特征参数,最后通过动态时间归整(DTW)进行相似度识别。实验表明,本文算法对汉语清晰度诊断押韵测试(DRT)字表的测试结果高达92.90%,有良好的识别率。

基于MFCC提取和DTW优化的连续音频识别算法设计

介绍了一种新型的利用梅尔频率倒谱系数(MFCC)提取和动态时间规整技术(DTW)优化的连续音频识别算法。首先对数学原理与算法步骤进行设计与规划,使用大规模音频数据库进行预处理,经过时域和频域分析提取相应的特征;然后利用双门限法把连续音频切分为不同的音频块,并对切分部分进行针对性识别,将其与时频域数据库的模板进行匹配比对,实现了较好的连续音频识别效果,在时域和频域识别上的准确性均能达到89%。该研究成果可应用于钢琴教学系统的开发,尤其是在辅助学习者正确弹出曲谱方面具有广阔的应用前景。

基于改进语音信号双门限法端点检测算法的研究

语音信号端点检测是语音信号预处理过程中的重要环节,传统双门限法采用短时能量和短时平均过零率,通过设置阈值进行语音信号起始点和结束点检测,在高信噪比条件下识别效果较好,但是在低信噪比下,噪声影响传统检测的速度和准确性。为了提高语音识别的效率,提出一种改进的双门限法语音端点检测算法,采用动态设定阈值,进行平滑滤波等改进方法。通过MATLAB仿真表明,改进算法在低信噪比下的识别准确性较高,有利于后续语音识别的研究。

光纤监测系统入侵信号的检测和识别

为改善光纤监测系统监测信号特征复杂及入侵信号检测效率和识别精度低等问题,提出了一种改进的谱减法进行去噪处理,从而有效减少噪声干扰并获得具有高信噪比的监测信号;其次,由于光纤监测信号大部分为静默信号,因此对其进行端点检测从而有效提取入侵信号;最后,基于提取的入侵信号的时频域特征,提出了一种多特征融合深度学习识别模型,从而准确识别入侵类型.

基于异常声音的隧道交通事故检测方法

针对公路隧道内交通事故的动态感知问题,在传统检测方法的基础上引入声学检测理论与方法,研究基于异常声音检测的隧道交通事故智能检测方法。通过分析短时能量(short term energy,STE)和梅尔倒谱系数(Mel-scale frequency cepstral coefficients,MFCC)检测方法在事故段特征表征以及精度干扰方面的缺陷,提出1种改进的融合特征MFCCE研究隧道环境下的交通事故检测。提取STE和MFCC特征并使用主成分分析(principal component analysis,PCA)进行特征融合得到新的融合特征MFCCE。以真实行车事故数据为基础,构建包含刹车与碰撞声的2段隧道噪声实验样本数据,分别对应早高峰时段(07:00—08:00)及平峰时段(12:00-13:00)的行车条件对隧道内的事故环境进行模拟分析,利用端点检测对所提方法进行验证并与其余2种方法进行对比分析。使用Pearson简单相关系数法作为最终的评价方法,通过该方法计算得到的相关系数r对比三种检测结果与原始样本的正相关相性。实验结果表明:STE在平峰及早高峰时段的相关系数分别为0.933和0.988;MFCC在平峰及早高峰时段的相关系数均为0.998;而无论在平峰还是早高峰时段,MFCCE的相关系数(0.999)均高于另外其他2种检测方法。MFCCE的平均相关系数相比于其他2种检测方法(STE、MFCC)分别提高了3.95%和1.00%。