基于信号稀疏分解的水下回波分类

针对表面起伏不平的水下底质回波分类效果差的问题,提出一种新颖的基于信号稀疏分解理论的水下底质回波特征提取方法。本方法并不使用通用时频字典,而是针对回波分类这一中心任务直接采用回波训练样本集作为字典,将水下回波信号在该字典上进行稀疏分解,然后提取出回波信号的类别能量特征。对水下钴结壳等三类底质回波分类实验表明,基于信号稀疏分解的类别能量特征的fisher分布明显优于小波域模极大值边缘特征和奇异值特征,从而显著提高了水下回波的分类效果。研究结论:在回波特征提取阶段,采用回波样本作为信号表达字典是可行的,同时由回波样本字典引入的回波类别信息将有助于获取更优的回波特征。      

结合幅度谱和功率谱字典的语音增强方法

从双路字典学习、噪声功率谱估计、语音幅度谱重构角度提出了一种改进的谱特征稀疏表示语音增强方法。在字典学习阶段,融合功率谱与幅度谱特征,采用区分性字典降低语音字典和噪声字典的相干性;在语音增强阶段,提出一种噪声功率谱估计方法对非平稳噪声进行跟踪估计;考虑到幅度谱和功率谱特征对不同噪声的适应程度不同,设计了语音重构权值表。对分别由幅度谱和功率谱恢复而来的两路信号进行自适应加权重构,结合相位补偿函数得到增强后的语音信号。实验结果表明,该方法在平稳、非平稳噪声环境下相比于单一谱特征的语音增强方法平均提高31.6%,改善了语音增强方法的性能。      

多特征融合的鸟类物种识别方法

针对短时窗平均/长时窗平均算法从次声台站监测数据中提取的信号仍然包含噪声的问题,对支持向量机和人工神经网络的机器学习方法进行了研究。采用小波包分解的方法对信号进行重构,提取出各频带内的重构信号能量特征,对事件信号和噪声进行了识别实验,并分析了提高识别能力的方法,为工程应用提供理论参考。实验结果表明,在训练数据集不大的情况下,通过优化模型结构可以将两种方法的识别能力提高到可以接受的水平。     

联合稀疏表示在水下目标分类中的应用

水下目标分类识别的性能受所选特征的限制,多特征往往可以获得更加稳定的结果,针对这一问题,提出了一种基于联合稀疏表示模型的水下目标分类识别方法。首先对水下目标回波信号提取3种具有信息互补性与关联性的特征:中心矩特征、小波包能量谱特征、梅尔频率倒谱系数特征,然后应用加速近端梯度法对联合稀疏表示模型进行优化,求解得到最优联合稀疏系数,最后根据最小误差准则确定目标类别。在消声水池开展模拟实验,对6类目标进行分类识别,结果表明:与传统算法相比,提出的算法具有更高识别准确率,并且其执行效率较传统算法有很大提升。      

基于变分模态分解的语音情感识别方法*

针对传统语音情感特征参数在进行情感分类时性能不佳的问题,该文提出了一种基于变分模态分解的语音情感识别方法。情感语音信号首先由变分模态分解提取固有模态函数,然后对所选主导固有模态函数进行重新聚合,再提取梅尔倒谱系数和各固有模态函数的希尔伯特边际谱。为了验证该文提出的特征性能,选用两种语音数据库(EMODB、RAVDESS)进行实验,按该文方法提取特征后使用极限学习机进行语音情感分类识别。实验结果表明:相比基于经验模态分解和集合经验模态分解的语音情感特征,该文提出的特征有更好的识别性能,验证了该方法的实用性。     

基于稀疏表示的核素能谱特征提取及核素识别

提出了一种基于稀疏表示的核素能谱特征提取方法,其实质是将核素能谱在区分性最好的稀疏原子上进行投影。利用稀疏分解方法对核素能谱进行稀疏分解,提取分解系数向量作为表征核素的特征向量,通过模式识别分类方法建立分类模型实现核素识别。与传统稀疏分解方法的区别在于:在能谱稀疏分解过程中按照稀疏字典中的原子排列顺序顺次进行分解;其次,分解目的在于特征提取,即最终提取到的特征对不同核素具有可区分性,并不要求核素能谱的重构精度。在241Am, 133Ba, 60Co, 137Cs, 131I和152Eu共6种核素1200个能谱数据上进行了核素识别实验,7种不同分类算法的平均识别率达到91.71%,实验结果的统计分析表明,本文提出的特征提取方法识别准确率显著地高于两种传统核素能谱特征提取方法准确率。     

小波变换的EDXRF光谱金属组分特征峰位置识别

主要研究X射线荧光光谱金属组分特征谱位置的确定。依据不同金属组分的特征谱特性,分析了特征谱的选取规律,在奇异值分析理论和模极大值理论的基础上,分析了基于特征谱小波分解系数的模极大值提取方法,在不同分解尺度下的特点及其传播特性,提出了基于模极大值传播的区间特征峰筛选方法,并对实际测量光谱进行了实验分析。结果表明：利用bior4.4小波作为基函数对实验测量的全能谱数据进行4层小波变换,利用模极大值传播特性,可以消除全能谱上叠加的部分噪声对光谱分析造成的阶跃影响;为提高特征峰的位置识别概率,对小波变换中小于给定阈值的分解系数进行压缩,将实验获取的X射线荧光全能谱第4层小波分解系数直接进行特征峰识别,得到的677个峰值位置,压缩到186个;在此基础上,再采用模极大值传播的区间特征峰筛选方法,筛选区间初始值设置为600 eV,经识别得到的特征峰峰值位置仅为27个,识别准确率得到有效提高。     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

经验模态分解法在光纤光栅围栏报警系统中的应用

为了解决基于FBG传感器的光纤围栏报警系统中多个FBG传感器在同时受扰时,定位报警信号难的问题,实现对报警信号的有效识别和判断,提出了一种基于经验模态分解（EMD）和小波包特征熵算法的分析方法。利用经验模态分解法对于信号的突变性敏感和有效保留以及其特有的自适应性分解特性,首先对报警信号进行经验模态分解,再结合小波包分解,得到小波包系数提取其信号的能量分布,再做归一化得到信号的能量分布特征向量,进而运用相关性分析实现对报警信号的识别和判断。通过建立实验模型,对采集到的报警信号做了分析,证明了该方法对于解决光纤围栏报警系统中FBG传感器的级联判断报警信号的有效性。     

一种强噪声背景下微弱超声信号提取方法研究

为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.     

Deceptive Chinese speech detection based on sparse decomposition of cepstral feature

In order to improve the performance of deception detection based on Chinese speech signals, a method of sparse decomposition on spectral feature is proposed. First, the wavelet packet transform is applied to divide the speech signal into multiple sub-bands. Band cepstral features of wavelet packets are obtained by operating the discrete cosine transform on loga?rithmic energy of each sub-band. The cepstral feature is generated by combing Mel Frequency Cepstral Coefficient and Wavelet Packet Band Cepstral Coefficient. Second, K-singular value decomposition algorithm is employed to achieve the training of an over-complete mixture dictionary based on both the truth and deceptive feature sets, and an orthogonal matching pursuit algorithm is used for sparse coding according to the mixture dictionary to get sparse feature.Finally, recognition experiments axe performed with various classified modules. Experimental results show that the sparse decomposition method has better performance comparied with con?ventional dimension reduced methods. The recognition accuracy of the method proposed in this paper is 78.34%, which is higher than methods using other features, improving the recognition ability of deception detection system significantly.     

A structural health monitoring strategy using cepstral features

A statistical pattern recognition based damage detection algorithm is proposed. The algorithm is developed according to the training and testing scheme, typical of pattern recognition applications. The original contribution of the work is given by the use of an adaptation of Mel-Frequency Cepstral Coefficients as damage sensitive features, as their compactness and de-correlation characteristics make them particularly suited for statistical pattern recognition applications. At the same time, the ease of extraction, which requires minimal user expertise, represents an important advantage over other more popular features, and makes the cepstral features particularly convenient for implementation into automatic structural health monitoring routines. The damage detection algorithm employs the squared Mahalanobis distance to solve the Structural Health Monitoring assignment. The method is validated by using both simulated and experimental data, and the performance of said features is compared to that of Auto-Regressive (AR) coefficients, which have been largely used to solve the task of structural damage detection. The experimental data were measured on a steel frame, which behave nonlinearly in its damaged configuration, at the Los Alamos National Laboratory. Results demonstrate that the proposed approach may be conveniently used in real-life applications, since cepstral features outperform AR coefficients when dealing with experimental data modeled to mimic the operational and environmental variability.     

Classification of stop place in consonant-vowel contexts using feature extrapolation of acoustic-phonetic features in telephone speech

Knowledge-based speech recognition systems extract acoustic cues from the signal to identify speech characteristics. For channel-deteriorated telephone speech, acoustic cues, especially those for stop consonant place, are expected to be degraded or absent. To investigate the use of knowledge-based methods in degraded environments, feature extrapolation of acoustic-phonetic features based on Gaussian mixture models is examined. This process is applied to a stop place detection module that uses burst release and vowel onset cues for consonant-vowel tokens of English. Results show that classification performance is enhanced in telephone channel-degraded speech, with extrapolated acoustic-phonetic features reaching or exceeding performance using estimated Mel-frequency cepstral coefficients (MFCCs). Results also show acoustic-phonetic features may be combined with MFCCs for best performance, suggesting these features provide information complementary to MFCCs.     

卷积噪声环境下语音信号鲁棒特征提取

提出了一种基于独立分量分析(ICA)的语音信号鲁棒特征提取算法,用以解决在卷积噪声环境下语音信号的训练与识别特征不匹配的问题。该算法通过短时傅里叶变换将带噪语音信号从时域转换到频域后,采用复值ICA方法从带噪语音的短时谱中分离出语音信号的短时谱,然后根据所得到的语音信号短时谱计算美尔倒谱系数(MFCC)及其一阶差分作为特征参数。在仿真与真实环境下汉语数字语音识别实验中,所提算法相比较传统的MFCC其识别正确率分别提升了34.8%和32.6%。实验结果表明基于ICA方法的语音特征在卷积噪声环境下具有良好的鲁棒性。      

Features of underwater echo extraction based on signal sparse decomposition

In order to better realize sound echo recognition of underwater materials with heavily uneven surface,a features ion method based on the theory of signal sparse decomposition has been proposed.Instead of the common time frequency dictionary,sets of training echo samples are used directly as dictionary to realize echo sparse decomposition under L₁ optimization and a kind of energy features of the echo.Experiments on three kinds of bottom materials including the Cobalt Crust show that the Fisher distribution with this method is superior to that of edge features and of Singular Value Decomposition （SVD） features in wavelet domain.It means no doubt that much better classification result of underwater bottom materials can be obtained with the proposed energy features than the other two.It is concluded that echo samples used as a dictionary is feasible and the class information of echo introduced by this dictionary can help to obtain better echo features.     

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.     

稀疏低秩噪声模型下无监督实时单通道语音增强算法

针对现有基于字典学习的增强算法需要先验信息、不易实时处理的问题,提出一种便于实时处理的无监督的单通道语音增强算法。首先,该算法将无监督条件下背景噪声的建模问题转化为带噪语音幅度谱的稀疏低秩噪声分解;然后,采用增量非负子空间方法对背景噪声进行在线字典学习,获得能够体现背景噪声时变特性的自适应噪声字典;最后,利用所得的噪声字典,采用易于实时处理的逐帧迭代方式,对带噪语音进行处理。实验结果表明:相较于多带谱减法和基于低秩稀疏矩阵分解的增强算法,所提算法在噪声抑制方面的性能尤为显著,在多项性能评价指标上,均表现出更好的结果。