scholarly journals ANALYSIS OF THE STATE OF AIRCRAFT CREW MEMBER BY THE SPEECH USING GAUSSIAN MODELS OF MIXTURES

2021 ◽  
Vol 23 (1) ◽  
pp. 97-102
Author(s):  
N.A. Andriyanov ◽  
◽  
V.E. Dementiev ◽  
Keyword(s):  
Emotional State ◽  
Spectral Characteristics ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Crew Member ◽  
Civil Aviation ◽  
Signal Spectrum ◽  
Preliminary Training ◽  
Sampling Step ◽  
Multivariate Distributions

The work is devoted to the study of the effectiveness of the application of models of Gaussian mixtures for the recognition of abnormal deviations in the speaker's speech. The practical application of the developed algorithms for revealing the emotional state of the crew member by the phrase uttered by such crew member is proposed. The spectral characteristics of the speech signal are used as the main criterion for distinguishing using the Gaussian mixture model. In connection with a rather small sampling step in frequency and, accordingly, with the presence of 255 frequency components in the signal spectrum, it is proposed to compress the spectrum to 10 components. This approach made it possible to reduce the number of key parameters in the Gaussian model to 10, which, in turn, made it possible to simplify the analysis process when constructing multivariate distributions. To assess the quality of the proposed algorithm, test phrases were recorded. At the same time, various psychological states of the speaker were imitated. We used both simple unregulated speech structures and messages regulated in accordance with the Federal Aviation Rules when conducting radio exchange in civil aviation on the territory of the Russian Federation. Taking into account the limitations on the prior knowledge of the model and clustering by spectral characteristics, all recordings of the model were made by one speaker. Three classes of the speaker's emotional state were considered. At the output, the recognition system put such marks as a calm state, a tired state, a stressful state. Various states were artificially simulated during data preparation. On a test sample of 48 messages, a Gaussian model of 3 components and 10 parameters without preliminary training immediately allowed to achieve a result of about 65%, while the probability of recognizing the correct class with 3 equal classes a priori is 33%. As further research, it is proposed to apply preliminary training using neural networks or correlation algorithms. This approach will allow further clustering at a deeper level, when, for example, the gender of the speaker is determined, a typical message of the radio exchange is determined, and then the emotional state of the speaker is revealed.

DESIGN OF THE VIRTUAL DEVICE FOR THE SIGNAL SPECTRUM CALCULATION IN THE LABVIEW ENVIRONMENT

2019 ◽  
Author(s):  
Tossenko O.M.
Keyword(s):  
Virtual Instrument ◽  
Spectral Characteristics ◽  
Spectral Composition ◽  
Automated System ◽  
Signal Spectrum ◽  
Measuring Instruments ◽  
Specific Information ◽  
Measuring Device ◽  
Virtual Device ◽  
Graphical Tool

The development of measuring instruments requires a specialist to know the principles of operation of advanced measuring systems. This article describes guidelines for creating a virtual appliance in LabVIEW. LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical application programming environment used as a standard tool for measuring, analyzing their data, further ma­ naging devices and objects under study. LabVIEW language is not like other programming languages. It does not create a program, but a virtual tool, designed not only for the simulation of certain processes, but also for the management of hardware and the study of real physical objects. The article deals with the task of designing application software for a specific information-measuring device, analyzes the capabilities of the LabVIEW environment for spectral analysis of various signals, outlines the basic principles and techniques of programming within the framework of the LabVIEW graphical environment during the basic stages of development. The procedure for creating a virtual device is described, which allows to evaluate the spectral composition of the signals, presents a graphical code of execution (diagram) to the program and a graphical tool interface of the virtual device. A number of basic elements used to develop the program are described. The simplicity of the graphic designs, the ease of installation on the field of the program, the clarity and readability of the program — all of which makes LabVIEW preferred over other languages of programming. In most cases, the experiment is the only source of reliable information. And the result is achieved much faster than the methods of "pure" theory. The article substantiates the effectiveness of using a development tool that allows to obtain a software product and ensure the fulfillment of all the basic functions of an automated system. Developing a software algorithm for calculating statistical parameters will help engineering students understand the order of determining spectral characteristics and their place in the structure of experimental research.

scholarly journals The Current Spectrum Formation of a Non-Periodic Signal: A Differential Approach

Inventions ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 15
Author(s):  
Sergey Sokolov ◽  
Daniil Marshakov ◽  
Arthur Novikov
Keyword(s):  
Real Time ◽  
Fixed Time ◽  
Signal Spectrum ◽  
Time Interval ◽  
Periodic Signal ◽  
Periodic Functions ◽  
Computationally Efficient ◽  
Sampling Step ◽  
Current Spectrum ◽  
Original Definition

The paper deals with the problem of forming spectra of non-periodic signals in real-time. The disadvantage of the existing approaches is the dependence of the formed spectrum on time as a parameter and the possibility of obtaining the signal spectrum in its original definition only for a fixed time, as well as a high amount of computation. In this regard, a computationally efficient algorithm is proposed for forming a spectrum of non-periodic functions on a time interval that is constantly updated with a given sampling step, which ensures the invariance of the generated spectrum to time as a parameter. The algorithm is based on obtaining differential equations that are based on generalized differentiation with respect to a variable time interval of spectral components and their solving while using the fourth-order Runge–Kutta method. A numerical simulation of the developed algorithm was performed using the MATLAB mathematical modeling package using the example of a substantially non-linear function. Based on the practical results, a comparative evaluation of computational and time complexity has been performed in solving the problem. Based on the obtained experimental results, it is concluded that it is possible to effectively use the proposed algorithm to calculate the current spectrum of non-periodic functions with the requirement of small sampling steps, i.e., when calculating the spectrum in real-time.

Detection of Moving Objects Based on Mixture Gaussian Model

2014 ◽  
Vol 1039 ◽  
pp. 274-279
Author(s):  
Guang Hua Chen ◽  
Gui Zhi Sheng
Keyword(s):  
Moving Objects ◽  
Video Segmentation ◽  
Color Space ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Connected Components ◽  
Shadow Removal ◽  
Em Algorithms ◽  
Learning Rates ◽  
Components Analysis

The paper proposes an improved adaptive Gaussian mixture model (GMM) approach with online EM algorithms for updating, which solves the video segmentation problems carried by busy environment and illumination change. Different learning rates are set for foreground district and background district respectively, which improves the convergence speed of background model. A shadow removal scheme is also introduced for extracting complete moving objects. It is based on brightness distortion and chromaticity distortion in RGB color space. Morphological filtering and connected components analysis algorithm are also introduced to process the result of background subtraction. The experiment results show that the improved GMM has good accuracy and high adaptability in video segmentation. It can extract a complete and clear moving object when it is incorporated with shadow removal.

scholarly journals ℓp-STFT: A Robust Parameter Estimator of a Frequency Hopping Signal for Impulsive Noise

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1017
Author(s):  
Yang Su ◽  
Lina Wang ◽  
Yuan Chen ◽  
Xiaolong Yang
Keyword(s):  
Communication Networks ◽  
Impulsive Noise ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Frequency Hopping ◽  
Parameter Estimator ◽  
Time Frequency ◽  
Robust Parameter ◽  
Short Time ◽  
Stable Noise

Impulsive noise is commonly present in many applications of actual communication networks, leading to algorithms based on the Gaussian model no longer being applicable. A robust parameter estimator of frequency-hopping (FH) signals suitable for various impulsive noise environments, referred to as ℓp-STFT, is proposed. The ℓp-STFT estimator replaces the ℓ2-norm by using the generalized version ℓp-norm where 1 < p < 2 for the derivation of the short-time Fourier transform (STFT) as an objective function. It combines impulsive noise processing with any time-frequency analysis algorithm based on STFT. Considering the accuracy of parameter estimation, the double-window spectrogram difference (DWSD) algorithm is used to illustrate the suitability of ℓp-STFT. Computer simulations are mainly conducted in α-stable noise to compare the performance of ℓp-STFT with STFT and fractional low-order STFT (FLOSTFT), Cauchy noise, and Gaussian mixture noise as supplements of different background noises to better demonstrate the robustness and accuracy of ℓp-STFT.

APPLICATION OF KOTELNIKOV’S THEOREM TO ANALYSIS OF RANDOM PROCESSES

2019 ◽  
pp. 28-34
Author(s):  
O. V. Goriunov ◽  
S. V. Slovtsov
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Random Process ◽  
Spectral Characteristics ◽  
Random Processes ◽  
Statistical Characteristics ◽  
Signal Spectrum ◽  
The Fourier Transform ◽  
The Stability ◽  
Correlation And Spectral Analysis

Analysis of many dynamic tasks arising in engineering applications is associated with the construction of spectral characteristics. However, the application of spectral analysis to random oscillations, which in most cases describe real processes (technical, technological, etc.), has a number of features and limitations associated, in particular, with the anconvergence of the Fourier transform. The substantiated metrological evaluation of the spectra associated with the reliability of the applied results is complicated by the absence of a rigorous mathematical model of a random process. The above remarks were solved on the basis of application of Kotelnikov's theorem at decomposition of a random process on known eigenfunctions. The obtained decomposition allowed us to obtain a number of results in the field of correlation and spectral analysis of random processes: the stability of the ACF and the relationship with the statistical characteristics of the implementation is proved, the orthogonal decomposition of the random process in the form of a continuous function is presented, which allows us to consider the evaluation and analyze the characteristics of the realizations without the use of a fast Fourier transform; the natural relationship between ACF and spectral density for a time-limited signal is shown, and the symmetric form of recording the signal spectrum is justified.

scholarly journals Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

Geosciences ◽  
2018 ◽  
Vol 8 (11) ◽  
pp. 388 ◽  
Author(s):  
Mattia Aleardi
Keyword(s):  
Acoustic Impedance ◽  
Statistical Models ◽  
Reservoir Characterization ◽  
A Priori ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Correct Choice ◽  
Joint Estimation ◽  
Coverage Ratio ◽  
Non Parametric

We discuss the influence of different statistical models in the prediction of porosity and litho-fluid facies from logged and inverted acoustic impedance (Ip) values. We compare the inversion and classification results that were obtained under three different statistical a-priori assumptions: an analytical Gaussian distribution, an analytical Gaussian-mixture model, and a non-parametric mixtu re distribution. The first model assumes Gaussian distributed porosity and Ip values, thus neglecting their facies-dependent behaviour related to different lithologic and saturation conditions. Differently, the other two statistical models relate each component of the mixture to a specific litho-fluid facies, so that the facies-dependency of porosity and Ip values is taken into account. Blind well tests are used to validate the final predictions, whereas the analysis of the maximum-a-posteriori (MAP) solutions, the coverage ratio, and the contingency analysis tools are used to quantitatively compare the inversion outcomes. This work points out that the correct choice of the statistical petrophysical model could be crucial in reservoir characterization studies. Indeed, for the investigated zone, it turns out that the simple Gaussian model constitutes an oversimplified assumption, while the two mixture models provide more accurate estimates, although the non-parametric one yields slightly superior predictions with respect to the Gaussian-mixture assumption.

scholarly journals Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chin-Teng Lin ◽  
Chih-Sheng Huang ◽  
Wen-Yu Yang ◽  
Avinash Kumar Singh ◽  
Chun-Hsiang Chuang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Correlation Analysis ◽  
Real Time ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Body Movement ◽  
Spectral Characteristics ◽  
Gaussian Mixture ◽  
Eeg Signals ◽  
Eeg Recordings

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

scholarly journals Mixture of GANs for Clustering

2018 ◽  
Author(s):  
Yang Yu ◽  
Wen-Ji Zhou
Keyword(s):  
Mixture Model ◽  
Expectation Maximization ◽  
Gaussian Model ◽  
Gaussian Mixture ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Gaussian Models ◽  
Promising Alternative ◽  
Adversarial Networks ◽  
Flexible Models

For data clustering, Gaussian mixture model (GMM) is a typical method that trains several Gaussian models to capture the data. Each Gaussian model then provides the distribution information of a cluster. For clustering of high dimensional and complex data, more flexible models rather than Gaussian models are desired. Recently, the generative adversarial networks (GANs) have shown effectiveness in capturing complex data distribution. Therefore, GAN mixture model (GANMM) would be a promising alternative of GMM. However, we notice that the non-flexibility of the Gaussian model is essential in the expectation-maximization procedure for training GMM. GAN can have much higher flexibility, which disables the commonly employed expectation-maximization procedure, as that the maximization cannot change the result of the expectation. In this paper, we propose to use the epsilon-expectation-maximization procedure for training GANMM. The experiments show that the proposed GANMM can have good performance on complex data as well as simple data.

scholarly journals Analysis of Different Statistical Models in Probabilistic Joint Estimation of Porosity and Litho-Fluid Facies from Acoustic Impedance Values

2018 ◽  
Author(s):  
Mattia Aleardi
Keyword(s):  
Acoustic Impedance ◽  
Statistical Models ◽  
Reservoir Characterization ◽  
Gaussian Model ◽  
Mixture Distribution ◽  
Gaussian Mixture ◽  
Correct Choice ◽  
Joint Estimation ◽  
Coverage Ratio ◽  
Non Parametric

We discuss the influence played by different statistical models in the prediction of porosity and litho-fluid facies from logged and post-stack inverted acoustic impedance (Ip) values. We compare the inversion and classification results obtained under three different a-priori statistical assumptions: an analytical Gaussian distribution, an analytical Gaussian-mixture model and a non-parametric mixture distribution. The first model assumes Gaussian distributed porosity and Ip values, thus neglecting their facies-dependent behaviour caused by different lithologic and saturation conditions. Differently, the other two statistical models relate each component of the mixture to a specific litho-fluid facies, so that the facies-dependency of porosity and Ip values is taken into account. Blind well tests are used to validate the final predictions, whereas the analysis of the maximum-a-posteriori (MAP) solutions, the coverage ratio and the contingency analysis tools are used to quantitatively compare the inversion outcomes. This work points out that the correct choice of the statistical petrophysical model could be crucial in reservoir characterization studies. Indeed, for the investigated zone it turns out that the simple Gaussian model constitutes an oversimplified assumption, while the two mixture models provide more accurate results, although the non-parametric one yields slightly superior predictions with respect to the Gaussian-mixture assumption.

Vehicle Detection Technology Based on Cascading Classifiers of Multi-Feature Integration

2017 ◽  
Vol 31 (10) ◽  
pp. 1750032 ◽  
Author(s):  
Xinyu Hu ◽  
Xuhui Ye ◽  
Daode Zhang ◽  
Liangyi Wu
Keyword(s):  
Intelligent Transportation System ◽  
Gaussian Model ◽  
Vehicle Detection ◽  
Gaussian Mixture ◽  
Classification Performance ◽  
Feature Integration ◽  
Detection Technology ◽  
Single Feature ◽  
Scanning Area ◽  
Region Extraction

Vehicle detection, as an important technology for urban intelligent transportation system, is having attracted increasingly interests of researchers in recent years. For the time cost problem of traditional road vehicles testing approach, a moving region extraction method based on Gaussian model is used to reduce the scanning area of the window, exclude some background noise and improve test speed. For the problem of traditional single feature, relatively lower detection rate and lack of ability to adapt to complex environment, a method based on the combination of Haar-like and 2bitBP (2bit Binary Pattern) features is adopted. Feature integration method enhances the expression of features. As a result, the improved classification performance of classifiers enables it to be adapted to different traffic environment. Firstly, a Gaussian mixture model is established to detect moving targets in overall region and then the Haar-like and 2bitBP features extraction are carried out in the region. At the end the action of cascading classification on samples achieve the detection of moving vehicles. The experimental results show that the method is effective for vehicle detection.

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