新型几何谱减语音增强方法

针对几何谱减算法在处理快速变化的语音时产生语音畸变的缺点,提出一种基于最小均方误差算法估计每帧语音信号的每一个频率分量上的平滑系数,产生自适应帧频率分量平滑系数代替固定值的平滑系数来估计先验信噪比,从而得到更加接近于真实情况的先验信噪比。通过计算板仓-斋藤距离,及利用仿真波形图、语谱图对算法进行客观测试,结果表明新算法相对其他谱减法在相同的去噪度下,语音畸变度最小且几乎察觉不到音乐噪声;特别是在低信噪比非平稳环境下,相对其他谱减法的优势更加显著。     

A graph-based expert system approach to geometric feature recognition

Geometric feature recognition is a crucial task in the development of concurrent engineering software. This paper presents a new methodology for geometric feature recognition which combines the advantages of face-edge adjacency graphs and expert systems. The methodology uses several new concepts such as enhanced winged edge data structure (EWEDS) and multi-attributed adjacency graphs (MAAG). The object model is presented as a set of facts. The rules for the recognition of each feature are derived from the corresponding feature-MAAG. This simplifies the process of writing the rules while enabling the inclusion of new features into the rule base as they are encountered.     

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.     

面向情感语音识别的非线性几何特征提取算法

针对现有时域、频域属性特征在区分情感状态上存在的局限性,提出一种基于相空间重构理论的非线性几何特征提取方法。首先,通过分析情感语音信号的最小延迟时间和嵌入维数来实现相空间重构;其次,在重构相空间下分析并提取基于轨迹描述轮廓的五种非线性几何特征;最后,结合韵律特征、MFCC特征和混沌特征,设计实验方案验证所提特征区分情感状态的能力并通过特征选择获得情感信息完整的最优特征集合。选用德语柏林语音库中的五种情感（高兴、悲伤、中性、愤怒、害怕）作为实验数据来源,支持向量机作为识别网络。实验结果表明：与韵律特征、MFCC特征和混沌特征相比,所提特征不仅可以有效地表征语音信号中的情感差异性,也能够弥补现有特征在刻画情感状态上的不足。     

Prediction-based geometric feature extraction for 2D laser scanner

This paper presents a novel algorithm for detecting line and circle features from 2D laser range scans. Unlike the conventional methods that use two stages for separating the features: data segmentation and feature separation in each segment, the proposed algorithm adopts a new structure and thus the computation complexity is much reduced. Moreover, it does not depend on prior knowledge of the environment, and it requires a minimum number of points per segment. We utilize prediction to achieve the above goals, so the algorithm is named prediction-based feature extraction (PFE). The efficiency and accuracy of the method is demonstrated by the experiments results.     

A novel geometric feature extraction method for ear recognition

The discriminative ability of geometric features can be well supported by empirical studies in ear recognition. Recently, a number of methods have been suggested for geometric feature extraction from ear images. However, these methods usually have relatively high feature dimension or are sensitive to rotation and scale variations. In this paper, we propose a novel geometric feature extraction method to address these issues. First, our studies show that the minimum Ear Height Line (EHL) is also helpful to characterize the contour of outer helix, and the combination of maximal EHL and minimum EHL can achieve better recognition performance. Second, we further extract three ratio-based features which are robust to scale variation. Our method has the feature dimension of six, and thus is efficient in matching for real-time ear recognition. Experimental results on two popular databases, i.e. USTB subset1 and IIT Delhi, show that the proposed approach can achieve promising recognition rates of 98.33% and 99.60%, respectively.     

Directional geometric histogram feature extraction and applications

Multimedia Tools and Applications - Image feature has been a hot research topic within the field of computer vision, with a wide scope of direct impacts on detection, recognition, image retrieval...     

An approach for feature semantics recognition in geometric models

This paper describes a method for the recognition of the semantics of parts (features) of a component from a pure geometric representation. It is suitable for verifying product life-cycle requirements from the early stages of the design process. The proposed method is appropriate to analyse B-rep geometric models, and it is not limited to models described by planar and cylindrical surfaces, but it can handle several types of face shapes. In this work the concept of semanteme is introduced. A semanteme represents the minimal element of engineering meaning that can be recognised in a geometric model. The semantemes recognised in a part of the model, which are potentially of engineering significance, are used to associate an engineering meaning to the part. This approach gives a wide flexibility to the proposed system, which is suitable to be used in different contexts of application, since it is possible to describe the reference context using the semanteme that the system can manage.In the paper the implemented prototype system is briefly described. The prototype system takes advantage of neutral interfaces that allow geometrical and topological information to be retrieved from a commercial CAD system.     

Kernel nonnegative matrix factorization for spectral EEG feature extraction

Nonnegative matrix factorization (NMF) seeks a decomposition of a nonnegative matrix X0 into a product of two nonnegative factor matrices U0 and V0, such that a discrepancy between X and UV is minimized. Assuming U=XW in the decomposition (for W0), kernel NMF (KNMF) is easily derived in the framework of least squares optimization. In this paper we make use of KNMF to extract discriminative spectral features from the time–frequency representation of electroencephalogram (EEG) data, which is an important task in EEG classification. Especially when KNMF with linear kernel is used, spectral features are easily computed by a matrix multiplication, while in the standard NMF multiplicative update should be performed repeatedly with the other factor matrix fixed, or the pseudo-inverse of a matrix is required. Moreover in KNMF with linear kernel, one can easily perform feature selection or data selection, because of its sparsity nature. Experiments on two EEG datasets in brain computer interface (BCI) competition indicate the useful behavior of our proposed methods.     

新的纹理图像特征提取方法

提出了一种新的基于非下采样Contourlet变换的纹理特征提取方法.首先对纹理图像进行非下采样Contourlet变换,然后提取不同尺度、不同方向上变换系数矩阵的均值和方差作为特征向量,大大降低了特征维数,并利用BP神经网络进行训练和仿真,实现了纹理图像的自动分类.实验结果表明,与小波包变换和改进的LBP纹理算子等方法相比,该方法能取得更好的分类效果.     

面向推荐系统的音乐特征抽取

音乐推荐系统是指根据用户的历史浏览数据,从候选库中推荐给用户可能喜欢的音乐的一种新型网络服务。该系统的关键在于需要对整个数据库按照音乐风格进行分类,基于此提出一种新的音乐特征处理方法来完成音乐库分类,以有效实现音乐推荐。该方法首先为候选音乐库构建常规的音乐特征数据集,然后基于分形理论对数据集进行属性约简,获取每一首音乐的推荐特征向量,并且依据特征向量的特点,定义了一种新的距离度量方法。在包含六种风格的音乐数据库的实验中,仿真结果证明了提出的音乐推荐特征和距离度量的有效性,与现有的基于内容的音乐检索研究相比,音乐推荐特征的使用极大地降低了对数据库存储量的需求,对音乐推荐系统的网络开发具有很好的应用价值。     

面向语音情感识别的语谱特征提取算法研究

语音情感识别的精度很大程度上取决于不同情感间的特征差异性。从分析语音的时频特性入手,结合人类的听觉选择性注意机制,提出一种基于语谱特征的语音情感识别算法。算法首先模拟人耳的听觉选择性注意机制,对情感语谱信号进行时域和频域上的分割提取,从而形成语音情感显著图。然后,基于显著图,提出采用Hu不变矩特征、纹理特征和部分语谱特征作为情感识别的主要特征。最后,基于支持向量机算法对语音情感进行识别。在语音情感数据库上的识别实验显示,提出的算法具有较高的语音情感识别率和鲁棒性,尤其对于实用的烦躁情感的识别最为明显。此外,不同情感特征间的主向量分析显示,所选情感特征间的差异性大,实用性强。     

基于曲率匹配的细胞几何形状特征提取方法

细胞轮廓的几何形状是细胞学涂片判读的重要参考,对研究宫颈病变的计算机辅助诊断具有重要意义。针对现有基于形状模板匹配的几何形状识别方法鲁棒性较差的问题,提出了基于曲率匹配的几何形状特征提取方法,通过比较模板轮廓和待识别轮廓的曲率,计算曲率曲线之间的相似度,进而得到细胞轮廓的形状特征,并采用依次旋转轮廓选取最佳匹配的方法来解决轮廓方向不一致的问题,采用以面积等效圆的半径比作为放大比率进行轮廓缩放的方法来解决轮廓大小不一致的问题。通过相关实验证明了该方法所提取的几何形状特征具有尺度不变性和旋转不变性,并与改进Hausdorff距离进行了实验对比,结果表明提取的形状特征能更加准确地识别出细胞轮廓的几何形状。     

基于KPLS的网络入侵特征抽取及检测方法

从特征抽取的角度研究提高入侵检测性能问题,提出应用核偏最小二乘(KPLS)进行入侵特征抽取和检测的方法.其优点在于KPLS能非线性地抽取输入特征的多个正交分量,并保持与输出类别的相关性,可同时完成入侵特征抽取和判别.将该方法应用于基于Linux主机的入侵检测实验,取得了比SVM和KPCR等方法更好的效果.     

KP-Miner: A keyphrase extraction system for English and Arabic documents

Automatic keyphrase extraction has many important applications including but not limited to summarization, cataloging/indexing, feature extraction for clustering and classification, and data mining. This paper presents the KP-Miner system, and demonstrates through experimentation and comparison with widely used systems that it is effective and efficient in extracting keyphrases from both English and Arabic documents of varied length. Unlike other existing keyphrase extraction systems, the KP-Miner system does not need to be trained on a particular document set in order to achieve its task. It also has the advantage of being configurable as the rules and heuristics adopted by the system are related to the general nature of documents and keyphrases. This implies that the users of this system can use their understanding of the document(s) being input into the system to fine-tune it to their particular needs.     

基于谱分析的无监督特征选择算法

为提高无监督状态下特征选择的准确度,基于图谱理论证明了标准化Laplacian矩阵前K个最小特征值分布表现了簇结构的可识别性,使用谱分析方法指导无监督特征选择,提出了特征重要度排序算法。对大容量数据集特征选择,应用Nystrm方法降低时间复杂度。实验结果表明,使用该算法与使用主流无监督特征选择方法及全部特征相比能得到更高的聚类性能评分。     

基于卡方统计的文本分类特征选择方法的研究

特征提取是文本分类过程中的一个重要环节,它的好坏将直接影响文本分类的准确率。在全面研究文本分类特征提取方法的基础上,分析了卡方统计的不足并提出了将频度、集中度、分散度应用到卡方统计方法上,对卡方统计进行改进,并通过实验对比改进前后的方法对文本分类效果的影响。在实验中,改进方法的分类效果要好于传统的方法,从而验证了改进方法的有效性和可行性。     

An automatic approach for ontology-based feature extraction from heterogeneous textualresources

Data mining algorithms such as data classification or clustering methods exploit features of entities to characterise, group or classify them according to their resemblance. In the past, many feature extraction methods focused on the analysis of numerical or categorical properties. In recent years, motivated by the success of the Information Society and the WWW, which has made available enormous amounts of textual electronic resources, researchers have proposed semantic data classification and clustering methods that exploit textual data at a conceptual level. To do so, these methods rely on pre-annotated inputs in which text has been mapped to their formal semantics according to one or several knowledge structures (e.g. ontologies, taxonomies). Hence, they are hampered by the bottleneck introduced by the manual semantic mapping process. To tackle this problem, this paper presents a domain-independent, automatic and unsupervised method to detect relevant features from heterogeneous textual resources, associating them to concepts modelled in a background ontology. The method has been applied to raw text resources and also to semi-structured ones (Wikipedia articles). It has been tested in the Tourism domain, showing promising results.