The Experts below are selected from a list of 257754 Experts worldwide ranked by ideXlab platform
Jingdong Chen - One of the best experts on this subject based on the ideXlab platform.
-
optimal single channel noise reduction filtering matrices from the Pearson Correlation coefficient perspective
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain, where an estimate of a vector of the desired clean speech is achieved by filtering a frame of the noisy signal with a rectangular filtering matrix. The core issue with this problem formulation is then the estimation of the optimal filtering matrix. The squared Pearson Correlation coefficient (SPCC) is used. We show that different optimal filtering matrices can be derived by maximizing or minimizing the SPCCs between different signals. For example, maximizing the SPCC between the enhanced signal and the filtered speech gives the reduced-rankWiener and minimum distortion (MD) filtering matrices while minimizing the SPCC gives the minimum noise (MN) and another reduced-rank Wiener filtering matrices. Simulation results are presented to illustrate the properties of these filtering matrices.
-
examples of optimal noise reduction filters derived from the squared Pearson Correlation coefficient
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain. Based on some orthogonal decomposition developed recently and the squared Pearson Correlation coefficient (SPCC), several noise reduction filters are derived. We will show that the optimization of the SPCC leads to the Wiener, minimum variance distortionless response (MVDR), minimum noise (MN), minimum uncorrelated speech and noise (MUSN), and linearly constrained minimum variance (LCMV) filters. We also compare the Wiener and MVDR filters derived from the SPCC to their counterparts derived from the mean-square error (MSE) criterion. Simulations are provided to illustrate the performance of all the deduced noise reduction filters. Index Terms—Noise reduction, speech enhancement, squared Pearson Correlation coefficient (SPCC), optimal filters.
-
Using the Pearson Correlation coefficient to develop an optimally weighted cross relation based blind SIMO identification algorithm
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong ChenAbstract:Blind SIMO identification is challenging when additive noise is strong and for ill-conditioned/acoustic SIMO systems. A weighted cross relation (CR) algorithm presumably can be robust to noise but there lacks a practical way to define the weights. In this paper, the Pearson Correlation coefficient (PCC) is used to develop an optimally weighted CR algorithm, which is validated by simulations.
-
on the importance of the Pearson Correlation coefficient in noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise reduction, which aims at estimating a clean speech from noisy observations, has attracted a considerable amount of research and engineering attention over the past few decades. In the single-channel scenario, an estimate of the clean speech can be obtained by passing the noisy signal picked up by the microphone through a linear filter/transformation. The core issue, then, is how to find an optimal filter/transformation such that, after the filtering process, the signal-to-noise ratio (SNR) is improved but the desired speech signal is not noticeably distorted. Most of the existing optimal filters (such as the Wiener filter and subspace transformation) are formulated from the mean-square error (MSE) criterion. However, with the MSE formulation, many desired properties of the optimal noise-reduction filters such as the SNR behavior cannot be seen. In this paper, we present a new criterion based on the Pearson Correlation coefficient (PCC). We show that in the context of noise reduction the squared PCC (SPCC) has many appealing properties and can be used as an optimization cost function to derive many optimal and suboptimal noise-reduction filters. The clear advantage of using the SPCC over the MSE is that the noise-reduction performance (in terms of the SNR improvement and speech distortion) of the resulting optimal filters can be easily analyzed. This shows that, as far as noise reduction is concerned, the SPCC-based cost function serves as a more natural criterion to optimize as compared to the MSE.
Jacob Benesty - One of the best experts on this subject based on the ideXlab platform.
-
optimal single channel noise reduction filtering matrices from the Pearson Correlation coefficient perspective
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain, where an estimate of a vector of the desired clean speech is achieved by filtering a frame of the noisy signal with a rectangular filtering matrix. The core issue with this problem formulation is then the estimation of the optimal filtering matrix. The squared Pearson Correlation coefficient (SPCC) is used. We show that different optimal filtering matrices can be derived by maximizing or minimizing the SPCCs between different signals. For example, maximizing the SPCC between the enhanced signal and the filtered speech gives the reduced-rankWiener and minimum distortion (MD) filtering matrices while minimizing the SPCC gives the minimum noise (MN) and another reduced-rank Wiener filtering matrices. Simulation results are presented to illustrate the properties of these filtering matrices.
-
examples of optimal noise reduction filters derived from the squared Pearson Correlation coefficient
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain. Based on some orthogonal decomposition developed recently and the squared Pearson Correlation coefficient (SPCC), several noise reduction filters are derived. We will show that the optimization of the SPCC leads to the Wiener, minimum variance distortionless response (MVDR), minimum noise (MN), minimum uncorrelated speech and noise (MUSN), and linearly constrained minimum variance (LCMV) filters. We also compare the Wiener and MVDR filters derived from the SPCC to their counterparts derived from the mean-square error (MSE) criterion. Simulations are provided to illustrate the performance of all the deduced noise reduction filters. Index Terms—Noise reduction, speech enhancement, squared Pearson Correlation coefficient (SPCC), optimal filters.
-
Using the Pearson Correlation coefficient to develop an optimally weighted cross relation based blind SIMO identification algorithm
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong ChenAbstract:Blind SIMO identification is challenging when additive noise is strong and for ill-conditioned/acoustic SIMO systems. A weighted cross relation (CR) algorithm presumably can be robust to noise but there lacks a practical way to define the weights. In this paper, the Pearson Correlation coefficient (PCC) is used to develop an optimally weighted CR algorithm, which is validated by simulations.
-
on the importance of the Pearson Correlation coefficient in noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise reduction, which aims at estimating a clean speech from noisy observations, has attracted a considerable amount of research and engineering attention over the past few decades. In the single-channel scenario, an estimate of the clean speech can be obtained by passing the noisy signal picked up by the microphone through a linear filter/transformation. The core issue, then, is how to find an optimal filter/transformation such that, after the filtering process, the signal-to-noise ratio (SNR) is improved but the desired speech signal is not noticeably distorted. Most of the existing optimal filters (such as the Wiener filter and subspace transformation) are formulated from the mean-square error (MSE) criterion. However, with the MSE formulation, many desired properties of the optimal noise-reduction filters such as the SNR behavior cannot be seen. In this paper, we present a new criterion based on the Pearson Correlation coefficient (PCC). We show that in the context of noise reduction the squared PCC (SPCC) has many appealing properties and can be used as an optimization cost function to derive many optimal and suboptimal noise-reduction filters. The clear advantage of using the SPCC over the MSE is that the noise-reduction performance (in terms of the SNR improvement and speech distortion) of the resulting optimal filters can be easily analyzed. This shows that, as far as noise reduction is concerned, the SPCC-based cost function serves as a more natural criterion to optimize as compared to the MSE.
Yiteng Huang - One of the best experts on this subject based on the ideXlab platform.
-
Using the Pearson Correlation coefficient to develop an optimally weighted cross relation based blind SIMO identification algorithm
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong ChenAbstract:Blind SIMO identification is challenging when additive noise is strong and for ill-conditioned/acoustic SIMO systems. A weighted cross relation (CR) algorithm presumably can be robust to noise but there lacks a practical way to define the weights. In this paper, the Pearson Correlation coefficient (PCC) is used to develop an optimally weighted CR algorithm, which is validated by simulations.
-
on the importance of the Pearson Correlation coefficient in noise reduction
IEEE Transactions on Audio Speech and Language Processing, 2008Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng HuangAbstract:Noise reduction, which aims at estimating a clean speech from noisy observations, has attracted a considerable amount of research and engineering attention over the past few decades. In the single-channel scenario, an estimate of the clean speech can be obtained by passing the noisy signal picked up by the microphone through a linear filter/transformation. The core issue, then, is how to find an optimal filter/transformation such that, after the filtering process, the signal-to-noise ratio (SNR) is improved but the desired speech signal is not noticeably distorted. Most of the existing optimal filters (such as the Wiener filter and subspace transformation) are formulated from the mean-square error (MSE) criterion. However, with the MSE formulation, many desired properties of the optimal noise-reduction filters such as the SNR behavior cannot be seen. In this paper, we present a new criterion based on the Pearson Correlation coefficient (PCC). We show that in the context of noise reduction the squared PCC (SPCC) has many appealing properties and can be used as an optimization cost function to derive many optimal and suboptimal noise-reduction filters. The clear advantage of using the SPCC over the MSE is that the noise-reduction performance (in terms of the SNR improvement and speech distortion) of the resulting optimal filters can be easily analyzed. This shows that, as far as noise reduction is concerned, the SPCC-based cost function serves as a more natural criterion to optimize as compared to the MSE.
Gongping Huang - One of the best experts on this subject based on the ideXlab platform.
-
optimal single channel noise reduction filtering matrices from the Pearson Correlation coefficient perspective
International Conference on Acoustics Speech and Signal Processing, 2015Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain, where an estimate of a vector of the desired clean speech is achieved by filtering a frame of the noisy signal with a rectangular filtering matrix. The core issue with this problem formulation is then the estimation of the optimal filtering matrix. The squared Pearson Correlation coefficient (SPCC) is used. We show that different optimal filtering matrices can be derived by maximizing or minimizing the SPCCs between different signals. For example, maximizing the SPCC between the enhanced signal and the filtered speech gives the reduced-rankWiener and minimum distortion (MD) filtering matrices while minimizing the SPCC gives the minimum noise (MN) and another reduced-rank Wiener filtering matrices. Simulation results are presented to illustrate the properties of these filtering matrices.
-
examples of optimal noise reduction filters derived from the squared Pearson Correlation coefficient
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Jacob Benesty, Gongping Huang, Jingdong ChenAbstract:This paper studies the problem of single-channel noise reduction in the time domain. Based on some orthogonal decomposition developed recently and the squared Pearson Correlation coefficient (SPCC), several noise reduction filters are derived. We will show that the optimization of the SPCC leads to the Wiener, minimum variance distortionless response (MVDR), minimum noise (MN), minimum uncorrelated speech and noise (MUSN), and linearly constrained minimum variance (LCMV) filters. We also compare the Wiener and MVDR filters derived from the SPCC to their counterparts derived from the mean-square error (MSE) criterion. Simulations are provided to illustrate the performance of all the deduced noise reduction filters. Index Terms—Noise reduction, speech enhancement, squared Pearson Correlation coefficient (SPCC), optimal filters.
Tyson Welzel - One of the best experts on this subject based on the ideXlab platform.
-
predicting mortality rates comparison of an administrative predictive model hospital standardized mortality ratio with a physiological predictive model acute physiology and chronic health evaluation iv a cross sectional study
Journal of Critical Care, 2016Co-Authors: Rene Elaine Toua, Jacques Erasmus De Kock, Tyson WelzelAbstract:Abstract Introduction Direct comparison of mortality rates has limited value because most deaths are due to the disease process. Predicting the risk of death accurately remains a challenge. Methods A cross-sectional study compared the expected mortality rate as calculated with an administrative model to a physiological model, Acute Physiology and Chronic Health Evaluation IV. The combined cohort and stratified samples ( 0.5 predicted mortality) were considered. A total of 47,982 patients were scored from 1 July 2013 to 30 June 2014, and 46,061 records were included in the analysis. Results A moderate Correlation was shown for the combined cohort (Pearson Correlation index, 0.618; 95% confidence interval [CI], 0.380-0.779; R2 = 0.38). A very good Correlation for the less than 10% stratum (Pearson Correlation index, 0.884; R2 = 0.78; 95% CI, 0.79-0.937) and a moderate Correlation for 0.1 to 0.5 predicted mortality rates (Pearson Correlation index, 0.782; R2 = 0.61; 95% CI, 0.623-0.879). There was no significant positive Correlation for the greater than 50% predicted mortality stratum (Pearson Correlation index, 0.087; R2 = 0.007; 95% CI, − 0.23 to 0.387). Conclusion At less than 0.1, the models are interchangeable, but in spite of a moderate Correlation, greater than 0.1 hospital standardized mortality ratio cannot be used to predict mortality.