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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, 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Optimal single-channel noise reduction filtering matrices from the Pearson Correlation Coefficient perspective
    2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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, 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Examples of optimal noise reduction filters derived from the squared Pearson Correlation Coefficient
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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, 2009
    Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong Chen
    Abstract:

    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.

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, 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Optimal single-channel noise reduction filtering matrices from the Pearson Correlation Coefficient perspective
    2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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, 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Examples of optimal noise reduction filters derived from the squared Pearson Correlation Coefficient
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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, 2009
    Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong Chen
    Abstract:

    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.

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, 2009
    Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong Chen
    Abstract:

    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.

  • ICASSP - 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, 2009
    Co-Authors: Yiteng Huang, Jacob Benesty, Jingdong Chen
    Abstract:

    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, 2008
    Co-Authors: Jacob Benesty, Jingdong Chen, Yiteng Huang
    Abstract:

    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, 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Optimal single-channel noise reduction filtering matrices from the Pearson Correlation Coefficient perspective
    2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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, 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

  • ICASSP - Examples of optimal noise reduction filters derived from the squared Pearson Correlation Coefficient
    2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014
    Co-Authors: Jacob Benesty, Gongping Huang, Jingdong Chen
    Abstract:

    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.

Nguyen Xuan Vinh - One of the best experts on this subject based on the ideXlab platform.

  • reverse engineering genetic networks with time delayed s system model and Pearson Correlation Coefficient
    International Conference on Neural Information Processing, 2013
    Co-Authors: Ahsan Raja Chowdhury, Madhu Chetty, Nguyen Xuan Vinh
    Abstract:

    In almost all biological systems including genetic networks, the complex simultaneous interactions occurring amongst different organelles within a cell are both - instantaneous and time-delayed. Among the various modeling approaches, applied for inferring Gene Regulatory Network GRN, recently proposed Time-delayed S-System Model TDSS is capable of simultaneously represent both the instantaneous and time-delayed interactions. While the delay parameters are incorporated in the S-System model to propose TDSS, this open a new challenge in GRN reconstruction. This paper proposes a systematic approach to fit in various level of knowledge in the delay parameters during the reverse engineering process. Further, we have approximated the delay parameters with well-known statistical measure Pearson Correlation Coefficient. Experimental studies have been carried out considering two widely used synthetic networks with various delays and real-life network of Saccharomyces cerevisiae called IRMA. The results clearly exhibit the influence of incorporating knowledge in the parameter learning process.

  • ICONIP (2) - Reverse Engineering Genetic Networks with Time-Delayed S-System Model and Pearson Correlation Coefficient
    Neural Information Processing, 2013
    Co-Authors: Ahsan Raja Chowdhury, Madhu Chetty, Nguyen Xuan Vinh
    Abstract:

    In almost all biological systems including genetic networks, the complex simultaneous interactions occurring amongst different organelles within a cell are both - instantaneous and time-delayed. Among the various modeling approaches, applied for inferring Gene Regulatory Network GRN, recently proposed Time-delayed S-System Model TDSS is capable of simultaneously represent both the instantaneous and time-delayed interactions. While the delay parameters are incorporated in the S-System model to propose TDSS, this open a new challenge in GRN reconstruction. This paper proposes a systematic approach to fit in various level of knowledge in the delay parameters during the reverse engineering process. Further, we have approximated the delay parameters with well-known statistical measure Pearson Correlation Coefficient. Experimental studies have been carried out considering two widely used synthetic networks with various delays and real-life network of Saccharomyces cerevisiae called IRMA. The results clearly exhibit the influence of incorporating knowledge in the parameter learning process.