To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passi...To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).展开更多
Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk fac...Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes.展开更多
An enhanced expectation maximization （ with channel time variation is proposed for mobile EM） based iterative channel estimator for coping multiple input multi output orthogonal frequency division multiplexing （MIM...An enhanced expectation maximization （ with channel time variation is proposed for mobile EM） based iterative channel estimator for coping multiple input multi output orthogonal frequency division multiplexing （MIMO OFDM） systems. In the proposed scheme, the recursive least squares （RLS） algorithm is applied to track the time varying channel impulse response （CIR） within several symbols. By using the tracked time varying CIR, the ICI are constructed and then cancelled from the received signal, thus reducing their impactions on the channel estimation. Moreover, based on an o ver sampled complex exponential basis expansion model （ OCE BEM）, an improved channel predic tor is derived in order to improve the initial channel estimates accuracy of the iterative estimator. Simulation results show that ying scenarios with a smaller the proposed scheme outperforms the classic counterpart in time var cost of complexity.展开更多
基金supported by National Natural Science Foundation of China(NSFC)(No.61671075)Major Program of National Natural Science Foundation of China(No.61631003)。
文摘To overcome the limitations of conventional speech enhancement methods, such as inaccurate voice activity detector(VAD) and noise estimation, a novel speech enhancement algorithm based on the approximate message passing(AMP) is adopted. AMP exploits the difference between speech and noise sparsity to remove or mute the noise from the corrupted speech. The AMP algorithm is adopted to reconstruct the clean speech efficiently for speech enhancement. More specifically, the prior probability distribution of speech sparsity coefficient is characterized by Gaussian-model, and the hyper-parameters of the prior model are excellently learned by expectation maximization(EM) algorithm. We utilize the k-nearest neighbor(k-NN) algorithm to learn the sparsity with the fact that the speech coefficients between adjacent frames are correlated. In addition, computational simulations are used to validate the proposed algorithm, which achieves better speech enhancement performance than other four baseline methods-Wiener filtering, subspace pursuit(SP), distributed sparsity adaptive matching pursuit(DSAMP), and expectation-maximization Gaussian-model approximate message passing(EM-GAMP) under different compression ratios and a wide range of signal to noise ratios(SNRs).
基金Sponsored by the National Natural Science Foundation of China(60671008)the National Science and Technology Support Project(2006038070031)the National"863"Program Project(2006AA02Z429)
文摘Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes.
基金Supported by the National Natural Science Foundation of China(6096200161071088)
文摘An enhanced expectation maximization （ with channel time variation is proposed for mobile EM） based iterative channel estimator for coping multiple input multi output orthogonal frequency division multiplexing （MIMO OFDM） systems. In the proposed scheme, the recursive least squares （RLS） algorithm is applied to track the time varying channel impulse response （CIR） within several symbols. By using the tracked time varying CIR, the ICI are constructed and then cancelled from the received signal, thus reducing their impactions on the channel estimation. Moreover, based on an o ver sampled complex exponential basis expansion model （ OCE BEM）, an improved channel predic tor is derived in order to improve the initial channel estimates accuracy of the iterative estimator. Simulation results show that ying scenarios with a smaller the proposed scheme outperforms the classic counterpart in time var cost of complexity.
