Automatic identification of audio recordings based on statistical modeling

This paper describes a methodology for the automatic identification of audio recordings of ethnic music. The identification is based on an application of hidden Markov models (HMMs), which are automatically built from a representation of the music pieces to be identified. States of the HMMs are labeled with music events, and the transition and observation probabilities are directly computed from the information on the music piece. The recordings are modeled by a set of acoustic features that are computed according with the characteristics of the music events. Three alternative approaches, based on typical applications of HMMs, are proposed to perform the identification. Tests carried out on collections of recordings showed that the methodology can achieve good results, and the identification rate is high enough to suggest applications for automatic retrieval of metadata and for the identification of alternative recordings of the same piece.     

利用参数统计方法自动识别数字调制信号

自动调制方式识别应用范围广泛，对于军用软件无线电侦察接收机更具有十分重要的意义。本文提出了一种基于统计参数识别信号类型的新方法，在满足SNR≥10dB的条件下，可正确识别2ASK、M－AM、QAM、PSK、FSK等五种信号。仿真试验表明：计算简单，识别效果良好。     

Real-time demodulation of signals using statistical methods

A novel approach to the uncommitted, real-time demodulation of signals using statistical classifiers is described. Preliminary results are presented together with a brief discussion of current work.     

ESD protection device design using statistical methods

This paper describes a design of the electrostatic discharge (ESD) protection device to minimize its area A_p while maintaining the breakdown voltage V_ESD. Hypothesis tests using measured data were performed to find the severest applied serge condition and to select control factors for the design-of-experiments (DOE). Also, technology CAD (TCAD) was used to estimate V_ESD. An optimum device structure, where salicide block was employed, was found using statistical methods and TCAD.     

超声红外热像技术中缺陷的自动识别

超声红外热像技术是一种新型无损检测技术,对金属试件疲劳裂纹、复合材料冲击损伤等缺陷具有良好的检测效果。传统缺陷识别主要依靠肉眼识别和专业经验,对缺陷类型、缺陷程度的判断很难定量把握;基于时间序列的缺陷识别算法速度慢、精度差、自动化程度低。本文以超声红外热像技术中裂纹的检测为例,通过对比分析热图像分割区域中裂纹区域与亮点区域的形状、灰度等分布特征,提取了用于裂纹信息识别的特征参量,开发了基于加权支持向量机的裂纹自动识别算法,为实现超声红外热像技术中缺陷的自动识别奠定了基础。试验验证了本文所提特征参量和自动识别算法的有效性。     

Evaluation of radiation fields using statistical methods of integration

An improved Monte Carlo method of integration is used to evaluate the radiation fields of a large linear antenna and a large aperture antenna. When compared with the conventional numerical methods of integration, the improved Monte Carlo method saves a considerable amount of effort and time, yet it still gives reasonably accurate results.     

Automatic aircraft landing using interferometric inverse syntheticaperture radar imaging

This paper presents an interferometric processing of an aircraft's monostatic and bistatic inverse synthetic aperture radar (ISAR) signatures for automatic landing. The aircraft's squint angle in this ISAR imaging problem is near 90 degrees . We show that this extreme squint angle does not pose any problem for the ISAR Fourier-based (wavefront) reconstruction algorithm. In fact, the aircraft can be imaged accurately, and without any erroneous shifts in the cross-range domain, within the imposed theoretical resolution. Moreover, the algorithm is accurate enough such that one can utilize the phase of the ISAR monostatic and bistatic measurements for interferometric processing. The resultant interferometric ISAR image is used to detect undesirable rotations in the aircraft's orientation.     

Automatic identification of functional clusters in FMRI data using spatial dependence

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a detailed functional segmentation of brain. However, such high-order segmentation does not establish the relationships among different brain networks, and also studying and classifying components can be challenging. In this study, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In our MICA framework, stable components are hierarchically grouped into clusters based on higher order statistical dependence--mutual information--among spatial components, instead of the typically used temporal correlation among time courses. The final cluster membership is determined using a statistical hypothesis testing method. Since ICA decomposition takes into account the modulation of the spatial maps, i.e., temporal information, our ICA-based approach incorporates both spatial and temporal information effectively. Our experimental results from both simulated and real fMRI datasets show that the use of spatial dependence leads to physiologically meaningful connectivity structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid, arteries, and large draining veins, are grouped together and encouragingly distinguished from other components of interest.     

Automatic identification of voice band telephony coding schemes using neural networks

A new method for identifying the type of coding present on a telecommunications channel is described. The method employs a combination of least mean squares adaptive filter techniques and neural network methods. The method is explained, and simulation results presented.<>     

Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration

In this paper, we show how the concept of statistical deformation models (SDMs) can be used for the construction of average models of the anatomy and their variability. SDMs are built by performing a statistical analysis of the deformations required to map anatomical features in one subject into the corresponding features in another subject. The concept of SDMs is similar to statistical shape models (SSMs) which capture statistical information about shapes across a population, but offers several advantages over SSMs. First, SDMs can be constructed directly from images such as three-dimensional (3-D) magnetic resonance (MR) or computer tomography volumes without the need for segmentation which is usually a prerequisite for the construction of SSMs. Instead, a nonrigid registration algorithm based on free-form deformations and normalized mutual information is used to compute the deformations required to establish dense correspondences between the reference subject and the subjects in the population class under investigation. Second, SDMs allow the construction of an atlas of the average anatomy as well as its variability across a population of subjects. Finally, SDMs take the 3-D nature of the underlying anatomy into account by analysing dense 3-D deformation fields rather than only information about the surface shape of anatomical structures. We show results for the construction of anatomical models of the brain from the MR images of 25 different subjects. The correspondences obtained by the nonrigid registration are evaluated using anatomical landmark locations and show an average error of 1.40 mm at these anatomical landmark positions. We also demonstrate that SDMs can be constructed so as to minimize the bias toward the chosen reference subject.     

Computer-intensive methods in statistical analysis

As far back as the late 1970s, the impact of affordable, high-speed computers on the theory and practice of modern statistics was recognized by Efron (1979, 1982). As a result, the bootstrap and other computer-intensive statistical methods (such as subsampling and the jackknife) have been developed extensively since that time and now constitute very powerful (and intuitive) tools to do statistics with. This article provides a readable, self-contained introduction to the bootstrap and jackknife methodology for statistical inference; in particular, the focus is on the derivation of confidence intervals in general situations. A guide to the available bibliography on bootstrap methods is also offered     

Improved methods for the blind system identification using higherorder statistics

It is demonstrated by a detailed analysis of blind system identification that under specific system configurations, a recently published least-squares algorithm shows a poor convergence behavior, especially if the system order is overdetermined. To overcome these problems, a supplementary condition is introduced that guarantees proper convergence in most cases. An alternative approach for the blind identification of mixed-phase systems, the so-called cumulant zero-matching method, is presented. In this approach, the solution of a set of nonlinear equations, which is necessary in the least-squares method, is replaced by the calculation of zeros of polynomials. The main advantage over the least-squares solution is that overdetermination of the system order is rather harmless, since it only results in additional zeros in the origin of the z-plane. The different methods for system identification presented are illustrated by simulation results     

Automatic identification of sound recordings

We have designed an automatic song identification component of a digital rights management (DRM) system for music, called MusicDNA. The details of MusicDNA are presented and the process by which we arrived at the final system configuration is outlined. This paper documents our experiences so that it can serve as a reference to those seeking to build a complete DRM system for music.     

Continuous speech recognition by statistical methods

Statistical methods useful in automatic recognition of continuous speech are described. They concern modeling of a speaker and of an acoustic processor, extraction of the models' statistical parameters and hypothesis search procedures and likelihood computations of linguistic decoding. Experimental results are presented that indicate the power of the methods.     

A statistical analysis of phase aberration correction using image quality factors in coherent imaging systems

A fundamental analysis of phase aberration correction techniques which use speckle brightness-based image equality factors (QFs) to iteratively reduce phase errors is presented. Phase aberration arises from the spatial inhomogeneity of acoustic velocity in human tissue and degrades the performance of diagnostic ultrasonic imaging systems. A theoretical analysis is presented indicating that the mean speckle brightness decreases with root-mean-square (RMS) phase error. A general definition of QFs is given using the probability of error as a criterion of performance. The QF is optimized through minimization of the probability of error under different conditions. The analysis provides a theoretical framework for the current correction technique using QFs under a variety of conditions, and is a useful tool to evaluate new QFs and correction techniques.     

Automatic fake document identification and localization using DE-Net and color-based features of foreign inks

Document examination is a vital mission for revealing illegal modifications that assist in the detection and resolution of criminal acts. Addition and alteration are more frequently used in handwritten documents. However, most of the documents have been modified with similar inks, and it is tough to detect or observe them with human eyes. As a result, there is a need for methods to automatically detect handwriting forgery to reach an accurate detection efficiently. In this paper, a novel and efficient method is proposed for automatically detecting altered handwritten documents and locating the fake part. Therefore, DE-Net is proposed to identify the forged document using a digitally scanned version of the document. Unlike the existing methods, a further localization schema is applied to locate the forged parts in the candidate forged document accurately. Where each forged document is segmented into objects. Color histograms of R, G, and B channels are used to generate a fused feature vector for each object. Then a structural similarity index (SSIM) is applied to detect the lower similarity parts as forged. The experimental results demonstrate that the proposed method can identify and localize foreign ink in handwritten documents with high performance.     

Automatic gait recognition based on statistical shape analysis

Gait recognition has recently gained significant attention from computer vision researchers. This interest is strongly motivated by the need for automated person identification systems at a distance in visual surveillance and monitoring applications. The paper proposes a simple and efficient automatic gait recognition algorithm using statistical shape analysis. For each image sequence, an improved background subtraction procedure is used to extract moving silhouettes of a walking figure from the background. Temporal changes of the detected silhouettes are then represented as an associated sequence of complex vector configurations in a common coordinate frame, and are further analyzed using the Procrustes shape analysis method to obtain mean shape as gait signature. Supervised pattern classification techniques, based on the full Procrustes distance measure, are adopted for recognition. This method does not directly analyze the dynamics of gait, but implicitly uses the action of walking to capture the structural characteristics of gait, especially the shape cues of body biometrics. The algorithm is tested on a database consisting of 240 sequences from 20 different subjects walking at 3 viewing angles in an outdoor environment. Experimental results are included to demonstrate the encouraging performance of the proposed algorithm.     

Frequency domain identification methods

Methods for estimating linear dynamical models from frequency data are studied, including the properties of frequency domain data generated by the discrete Fourier transform. The stochastic characteristics of the frequency domain data lead to a maximum likelihood (ML) formulation of the frequency domain estimation problem. Both discretetime and continuous time models are discussed. Consistency and variance of the ML estimate are described, and the connection with simpler frequency domain estimation schemes as well as the time domain ML method is pointed out.Supported by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT). This work was partly completed while the author was visiting the Department of Electrical and Computer Engineering, University of Newcastle, Australia.