Noisy voice detection algorithm based on feature stream fusion by Hua LONG, Mingliang YANG, Yubin SHAO

“…Aiming at the problem that the initial detection performance of voice segment was poor,and the voice continuity structure was damaged in voice communication,a noisy voice detection algorithm based on feature stream fusion was proposed.Firstly,the time domain feature stream,the spectral pattern feature stream and the statistical feature stream were extracted according to the voice characteristics.Secondly,the voice segment in the noisy audio was estimated by different voice feature streams.Finally,the voice prediction probability obtained by each feature stream was weighted and fused,and the voice estimation probability was processed in short time by the hidden Markov model.The performance test of composite voice database under the condition of multi-type noise and different signal-to-noise ratio shows that compared with the baseline model based on Bayesian and DNN classifier,the voice detection accuracy of the proposed algorithm is improved by 21.26% and 11.01% respectively,and the quality of target voice is significantly improved.…”
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Detecting Spatial Clusters via a Mixture of Dirichlet Processes by Meredith A. Ray, Jian Kang, Hongmei Zhang

“…Inferences of parameters including clustering were drawn under a Bayesian framework. Simulations were used to demonstrate and assess the method. …”
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Asynchronous perception algorithm based on energy detection by Pan YU, Bin LI, Cheng-lin ZHAO

“…In the future heterogeneous wireless networks,since primary user (PU) and cognitive secondary user (SU) are not coordinated to be synchronous,it will result in sense timing difference between PU’s transmitter and SU’s receiver.For this asynchronous sense case,a new asynchronous sensing algorithm based on Bayesian estimation theory was proposed.A unified dynamic state space model was first proposed to describe the observable energy relationship with dynamic PU state and unknown timing difference.Then,an iterative estimation scheme was designed using stochastic finite set and the rules of maximum posterior probability.Finally,approximated estimation results were obtained by using a particle filter.The simulation results show that the proposed asynchronous scheme significantly eliminates the uncertainty of the received signal information and thus improves the spectrum sensing performance by obtaining the time difference accurately.…”
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Reputation mechanism based on multi-path routing protocol in ad hoc networks by CUI Guo-hua, LU She-jie, LIU Zhi-yuan, GENG Yong-jun

“…When implement it,the Bayesian ap-proach was used to evaluate a node’s reputation. …”
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Asymptotic estimation for statistical models of continuous-time discrete martingales by Vaidotas Kanišauskas, Karolina Kanišauskienė

“…General conditions convenient for testing are determined to assure continuous validity of the maximum probability and Bayesian estimators, as well as the continuous asymptotic normality and the asymptotic minimaxity in each compact of the parametric set. …”
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Machine Learning Techniques for Classification of Stress Levels in Traffic by Amanda Trojan Fenerich, Egídio José Romanelli, Rodrigo Eduardo Catai, Maria Teresinha Arns Steiner

“…The classification algorithms used were Support Vector Machine (SVM), Bayesian Networks (BN), and Logistic Regression (LR), comparatively. …”
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A Cooperative Model for IS Security Risk Management in Distributed Environment by Nan Feng, Chundong Zheng

“…In the proposed model, the exchange of security information among the interconnected IS under distributed environment is supported by Bayesian networks (BNs). In addition, for an organization’s IS, a BN is utilized to represent its security environment and dynamically predict its security risk level, by which the security manager can select an optimal action to safeguard the firm’s information resources. …”
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Study of high-speed malicious Web page detection system based on two-step classifier by Zheng-qi WANG, Xiao-bing FENG, Chi ZHANG

“…In view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step malicious Web page detection.The first step of detection system was mainly used to filter a large number of normal Web pages,which was characterized by high efficiency,speed,update iteration easy,real rate priority.After the former filter,due to the limited number of samples,the main pursuit of the second step was the detection rate.The experimental results show that the proposed scheme can improve the detection speed of the system under the condition that the overall detection accuracy is basically the same,and can accept more detection requests in certain time.…”
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A new record of the genus Parabioxys (Hymenoptera, Braconidae, Aphidiinae) and a redescription of Bioxys japonicus from South Korea by Sangjin Kim, JuHyeong Sohn, Yerim Lee, Yeonghyeok Yu, Hyojoong Kim

“…Additionally, we present mitochondrial cytochrome c oxidase subunit I (COI) barcode region data and conduct a Bayesian tree analysis of closely related taxa.…”
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Decision Analysis via Granulation Based on General Binary Relation by M. M. E. Abd El-Monsef, N. M. Kilany

“…In rough set theory, the classification of objects according to approximation operators can be fitted into the Bayesian decision-theoretic model, with respect to three regions (positive, negative, and boundary region). …”
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Application-oriented resource matching model based on trust for internet-based virtual computing environment by Chun-ge ZHU, Xin-ran LIU, Yi-xian YANG, Hong ZHANG

“…In order to solve the problem of reasonable resource s ing in the virtual computing environment (iVCE),a resource matching model,based on trust “resource sliding window” model,was proposed.Firstly,the resources were classified in advance by their static properties.Then the resource nodes were evaluated by a trust model based on the time window and Bayesian algorithm,and the evaluated results as well as the resource load were fused to determine the levels of the dynamic real-time performance.Finally,the scheduling resources were allocated according to the comprehensive assessment of the static and dynamic properties of the resource nodes.The proposed model provides a basis of scheduling strategy for different resources with different tasks and attributes,thus achieving the goal of “proper resources’ serving proper tasks”.Simulation results show that,compared the previous scheduling algorithm,the proposed model has better performance in the success rate of task execution and the rate of resource utilization.…”
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Noise-robust linear prediction analysis of speech based on super-Gaussian excitation by Bin ZHOU, Xia ZOU, Xiong-wei ZHANG, Gai-hua ZHAO

“…To overcome the problem that the performance of the traditional linear prediction (LP) analysis of speech dete-riorates significantly in the presence of background noise,a novel algorithm for robust LP analysis of speech based on super-Gaussian excitation was proposed.The excitation noise of LP was modeled as a Student-t distribution,which was shown to be super-Gaussian.Then a novel probabilistic graphical model for robust LP analysis of speech was built by in-corporating the effect of additive noise explicitly.Furthermore,variational Bayesian inference was adopted to approxi-mate the intractable posterior distributions of the model parameters,based on which the LP coefficients of the noisy speech were estimated iteratively.The experimental results show that the developed algorithm performs well in terms of LP coefficients estimation of speech and is much more robust to ambient noise than several other algorithms.…”
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Network meta-analysis of cadmium toxicity against Chlorella vulgaris and the role of growth stimulants and macronutrients by M. Iqhrammullah, S. Saudah, M. Monalisa, F. Fahrurrozi, S.A. Akbar, S.S. Lubis

“…The collected data were then subjected to Bayesian frequentist network meta-analysis, utilizing standardized mean difference and a 95 percent confidence interval as measures of effect size. …”
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Transcription factor-dependent regulatory networks of sexual reproduction in Fusarium graminearum by Wonyong Kim, Da-Woon Kim, Zheng Wang, Meng Liu, Jeffrey P. Townsend, Frances Trail

“…Using transcriptomic data across stages of sexual development, we inferred a Bayesian network of these TFs that guided experiments to assess the robustness of gene interactions using a systems biology approach. …”
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Disproportionality analysis of upadacitinib-related adverse events in inflammatory bowel disease using the FDA adverse event reporting system by Shiyi Wang, Xiaojian Wang, Jing Ding, Xudong Zhang, Hongmei Zhu, Yihong Fan, Changbo Sun

“…This study evaluates upadacitinib-related adverse events (AEs) utilizing data from the US Food and Drug Administration Adverse Event Reporting System (FAERS).MethodsWe employed disproportionality analyses, including the proportional reporting ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) algorithms to identify signals of upadacitinib-associated AEs for treating inflammatory bowel disease (IBD).ResultsFrom a total of 7,037,004 adverse event reports sourced from the FAERS database, 37,822 identified upadacitinib as the primary suspect drug in adverse drug events (ADEs), including 1,917 reports specifically related to the treatment of inflammatory bowel disease (IBD). …”
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Joint Distribution pada Weighted Majority Vote (WMV) untuk Peningkatan Kinerja Sentiment Analysis Tersupervisi pada Dataset Twitter by Bagus Setya Rintyarna

“…Penelitian ini menggabungkan kinerja berbagai algoritma/experts seraya mengurangi tingkat kesalahan klasifikasi dengan meng-update bobot secara dinamis menggunakan weighted majority vote (WMV) berbasis joint distribution dari Bayesian Network. Pada tahap pertama, data di grabbing dari Twitter dengan 3 hashtag terkait Covid-19 sebagai data eksperimen. …”
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Visualizing the change of “viewpoints” in 3D virtual art exhibition by Kazuki Matsumoto, Takeshi Okada

“…Results from multiple statistical analyses, including hierarchical Bayesian analysis, indicated differences in view-angles and distances traveled while viewing the artworks under different experimental conditions (where different instructions were given prior to viewing). …”
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Object Tracking via 2DPCA and l2-Regularization by Haijun Wang, Hongjuan Ge, Shengyan Zhang

“…We present a fast and robust object tracking algorithm by using 2DPCA and l2-regularization in a Bayesian inference framework. Firstly, we model the challenging appearance of the tracked object using 2DPCA bases, which exploit the strength of subspace representation. …”
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A Nonlinear Prognostic Model Based on the Wiener Process with Three Sources of Uncertainty by Huifang Niu, Jianchao Zeng, Hui Shi, Bin Wang, Tianye Liu

“…Model parameters are then obtained by the expectation maximization (EM) algorithm, and the drift parameter is estimated adaptively using a Bayesian procedure. Finally, in order to illustrate the practical applications of the presented approach, a comparative study of real data on fatigue crack propagation is presented. …”
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Decentralized Kalman Filtering with Multilevel Quantized Innovation in Wireless Sensor Networks by Zhi Zhang, Jianxun Li

“…Besides, this paper explores the quantization state estimation by adopting the Bayesian method rather than the traditional iterated conditional expectation method. …”
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