A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spect...A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.展开更多
A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decompo...A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.展开更多
This paper proposes an efficient approximate Maximum Likelihood (ML) detection method for Multiple-Input Multiple-Output (MIMO) systems,which searches local area instead of exhaustive search and selects valid search p...This paper proposes an efficient approximate Maximum Likelihood (ML) detection method for Multiple-Input Multiple-Output (MIMO) systems,which searches local area instead of exhaustive search and selects valid search points in each transmit antenna signal constellation instead of all hy-perplane. Both of the selection and search complexity can be reduced significantly. The method per-forms the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.展开更多
The performance of the traditional Voice Activity Detection （VAD） algorithms declines sharply in lower Signal-to-Noise Ratio （SNR） environments. In this paper, a feature weighting likelihood method is proposed for...The performance of the traditional Voice Activity Detection （VAD） algorithms declines sharply in lower Signal-to-Noise Ratio （SNR） environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD. Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little. Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.展开更多
Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation res...Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dB better than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fading channels. For the consideration of practical application, it is further simplified in complexity. Compared with the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexity with small degradation in performance.展开更多
This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of ...This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).展开更多
The detection of stellar flares is crucial to understanding dynamic processes at the stellar surface and their potential impact on surrounding exoplanetary systems.Extensive time series data acquired by the Transiting...The detection of stellar flares is crucial to understanding dynamic processes at the stellar surface and their potential impact on surrounding exoplanetary systems.Extensive time series data acquired by the Transiting Exoplanet Survey Satellite(TESS)offer valuable opportunities for large-scale flare studies.A variety of methods is currently employed for flare detection,with machine learning(ML)approaches demonstrating strong potential for automated classification tasks,particularly for the analysis of astronomical time series.This review provides an overview of the methods used to detect stellar flares in TESS data and evaluates their performance and effectiveness.It includes our assessment of both traditional detection techniques and more recent methods,such as ML algorithms,highlighting their strengths and limitations.By addressing current challenges and identifying promising approaches,this manuscript aims to support further studies and promote the development of stellar flare research.展开更多
电压暂降是电能质量问题中的一个关键难题。为了更准确地检测电压暂降发生时刻,提出了基于最大似然的自适应扩展卡尔曼滤波EKF-ML(extended Kalman filter based on maximum likelihood)算法的检测方法。首先选取不同的状态向量,在电网...电压暂降是电能质量问题中的一个关键难题。为了更准确地检测电压暂降发生时刻,提出了基于最大似然的自适应扩展卡尔曼滤波EKF-ML(extended Kalman filter based on maximum likelihood)算法的检测方法。首先选取不同的状态向量,在电网信号中建立2种卡尔曼滤波系统模型;其次,利用最大似然自适应优化误差协方差矩阵R和Q以及初始条件参数;最后,引入不同电能质量扰动对电压暂降进行检测证明该方法的有效性。仿真结果表明:在谐波干扰、脉冲干扰以及不同信噪比干扰情况下,EKF-ML算法能实时准确地检测电压暂降起止时间。与已有的传统方法比较,该方法适合于在未知测量噪声的条件下对电压暂降进行检测。展开更多
基金supported by the KERI Primary Research Program through the Korea Research Council for Industrial Science & Technology funded by the Ministry of Science,ICT and Future Planning (No.15-12-N0101-46)
文摘A novel technique is proposed to improve the performance of voice activity detection(VAD) by using deep belief networks(DBN) with a likelihood ratio(LR). The likelihood ratio is derived from the speech and noise spectral components that are assumed to follow the Gaussian probability density function(PDF). The proposed algorithm employs DBN learning in order to classify voice activity by using the input signal to calculate the likelihood ratio. Experiments show that the proposed algorithm yields improved results in various noise environments, compared to the conventional VAD algorithms. Furthermore, the DBN based algorithm decreases the detection probability of error with [0.7, 2.6] compared to the support vector machine based algorithm.
文摘A blind adaptive scheme is proposed for joint maximum likelihood （ML） channel estimation and data detection of singleinput multiple-output （SIMO） systems. The joint ML optimisation over channel and data is decomposed into an iterative optimisation loop. An efficient global optimisation algorithm called the repeated weighted boosting search is employed at the upper level to optimally identify the unknown SIMO channel model, and the Viterbi algorithm is used at the lower level to produce the maximum likelihood sequence estimation of the unknown data sequence. A simulation example is used to demonstrate the effectiveness of this joint ML optimisation scheme for blind adaptive SIMO systems.
文摘This paper proposes an efficient approximate Maximum Likelihood (ML) detection method for Multiple-Input Multiple-Output (MIMO) systems,which searches local area instead of exhaustive search and selects valid search points in each transmit antenna signal constellation instead of all hy-perplane. Both of the selection and search complexity can be reduced significantly. The method per-forms the tradeoff between computational complexity and system performance by adjusting the neighborhood size to select the valid search points. Simulation results show that the performance is comparable to that of the ML detection while the complexity is only as the small fraction of ML.
基金Supported by the National Basic Research Program of China (973 Program) (No.2007CB311104)
文摘The performance of the traditional Voice Activity Detection （VAD） algorithms declines sharply in lower Signal-to-Noise Ratio （SNR） environments. In this paper, a feature weighting likelihood method is proposed for noise-robust VAD. The contribution of dynamic features to likelihood score can be increased via the method, which improves consequently the noise robustness of VAD. Divergence based dimension reduction method is proposed for saving computation, which reduces these feature dimensions with smaller divergence value at the cost of degrading the performance a little. Experimental results on Aurora Ⅱ database show that the detection performance in noise environments can remarkably be improved by the proposed method when the model trained in clean data is used to detect speech endpoints. Using weighting likelihood on the dimension-reduced features obtains comparable, even better, performance compared to original full-dimensional feature.
文摘Based on the Maximum-Likelihood (ML) criterion, this paper proposes a novel noncoherent detection algorithm for Orthogonal Multicode (OM) system in Nakagami fading channel. Some theoretical analysis and simulation results are presented. It is shown that the proposed ML algorithm is at least 0.7 dB better than the conventional Matched-Filter (MF) algorithm for uncoded systems, in both non-fading and fading channels. For the consideration of practical application, it is further simplified in complexity. Compared with the original ML algorithm, the simplified ML algorithm can provide significant reduction in complexity with small degradation in performance.
文摘This paper introduces a simple combining technique for cooperative relay scheme which is based on a Detect-and-Forward (DEF) relay protocol. Cooperative relay schemes have been introduced in earlier works but most of them ignore the quality of the source-relay (S-R) channel in the detection at the destination, although this channel can contribute heavily to the performance of cooperation schemes. For optimal detection, the destination has to account all possible error events at the relay as well. Here we present a Maximum Likelihood criterion (ML) at the destination which considers closed-form expressions for each symbol error rate (SER) to facilitate the detection. Computer simulations show that significant diversity gain and Packet Error Rate (PER) performance can be achieved by the proposed scheme with good tolerance to propagation errors from noisy relays. In fact, diversity gain is increased with additional relay nodes. We compare this scheme against the baseline Cooperative-Maximum Ratio Combining (C-MRC).
基金supported by the National Natural Science Foundation of China(12473104 and U2031144).
文摘The detection of stellar flares is crucial to understanding dynamic processes at the stellar surface and their potential impact on surrounding exoplanetary systems.Extensive time series data acquired by the Transiting Exoplanet Survey Satellite(TESS)offer valuable opportunities for large-scale flare studies.A variety of methods is currently employed for flare detection,with machine learning(ML)approaches demonstrating strong potential for automated classification tasks,particularly for the analysis of astronomical time series.This review provides an overview of the methods used to detect stellar flares in TESS data and evaluates their performance and effectiveness.It includes our assessment of both traditional detection techniques and more recent methods,such as ML algorithms,highlighting their strengths and limitations.By addressing current challenges and identifying promising approaches,this manuscript aims to support further studies and promote the development of stellar flare research.
文摘电压暂降是电能质量问题中的一个关键难题。为了更准确地检测电压暂降发生时刻,提出了基于最大似然的自适应扩展卡尔曼滤波EKF-ML(extended Kalman filter based on maximum likelihood)算法的检测方法。首先选取不同的状态向量,在电网信号中建立2种卡尔曼滤波系统模型;其次,利用最大似然自适应优化误差协方差矩阵R和Q以及初始条件参数;最后,引入不同电能质量扰动对电压暂降进行检测证明该方法的有效性。仿真结果表明:在谐波干扰、脉冲干扰以及不同信噪比干扰情况下,EKF-ML算法能实时准确地检测电压暂降起止时间。与已有的传统方法比较,该方法适合于在未知测量噪声的条件下对电压暂降进行检测。
