The Experts below are selected from a list of 55425 Experts worldwide ranked by ideXlab platform

Nasser Kehtarnavaz - One of the best experts on this subject based on the ideXlab platform.

  • from simulink to smartphone Signal Processing Application examples
    International Conference on Acoustics Speech and Signal Processing, 2015
    Co-Authors: Reza Pourrezashahri, Shane Parris, Fatemeh Saki, Issa M S Panahi, Nasser Kehtarnavaz
    Abstract:

    This paper presents the steps one needs to take in order to run a Signal Processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two Signal Processing Application examples from Simulink to smartphone. The Application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in Signal Processing, the approach presented in this paper is of benefit to practicing engineers and Signal Processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.

  • ICASSP - From Simulink to smartphone: Signal Processing Application examples
    2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015
    Co-Authors: Reza Pourreza-shahri, Shane Parris, Fatemeh Saki, Issa M S Panahi, Nasser Kehtarnavaz
    Abstract:

    This paper presents the steps one needs to take in order to run a Signal Processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two Signal Processing Application examples from Simulink to smartphone. The Application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in Signal Processing, the approach presented in this paper is of benefit to practicing engineers and Signal Processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.

Mohamed Najim - One of the best experts on this subject based on the ideXlab platform.

  • Dual H infini Algorithms for Signal Processing, Application to Speech Enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Nicolai Christov, Mohamed Najim
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this prob-lem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H filters. Once a new observation of the disturbed Signal is available, the first H algorithm uses the latest estimated parameters to estimate the Signal, while the second H algorithm uses the estimated Signal to update the parameters. In addition, as the H∞ filter behav-iour depends on the choice of various weights, we present a way to recursively tune them. This approach is then illustrated through the following cases: 1/ consistent estimation of the AR parameters from noisy observations, where the H scheme outperforms the existing algo-rithms; 2/ speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyse the relevance of our solution.

  • dual h _ infty algorithms for Signal Processing Application to speech enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Mohamed Najim, Nicolai Christov
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise in the state-space representation for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual Hinfin filters. Once a new observation of the disturbed Signal is available, the first Hinfin algorithm uses the latest estimated parameters to estimate the Signal, while the second Hinfin algorithm uses the estimated Signal to update the parameters. In addition, as the Hinfin filter behavior depends on the choice of various weights, we present a way to recursively tune them. This approach is then studied in the following cases: (1) consistent estimation of the AR parameters from noisy observations and (2) speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyze the relevance of our solution.

Nicolai Christov - One of the best experts on this subject based on the ideXlab platform.

  • Dual H infini Algorithms for Signal Processing, Application to Speech Enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Nicolai Christov, Mohamed Najim
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this prob-lem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H filters. Once a new observation of the disturbed Signal is available, the first H algorithm uses the latest estimated parameters to estimate the Signal, while the second H algorithm uses the estimated Signal to update the parameters. In addition, as the H∞ filter behav-iour depends on the choice of various weights, we present a way to recursively tune them. This approach is then illustrated through the following cases: 1/ consistent estimation of the AR parameters from noisy observations, where the H scheme outperforms the existing algo-rithms; 2/ speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyse the relevance of our solution.

  • dual h _ infty algorithms for Signal Processing Application to speech enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Mohamed Najim, Nicolai Christov
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise in the state-space representation for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual Hinfin filters. Once a new observation of the disturbed Signal is available, the first Hinfin algorithm uses the latest estimated parameters to estimate the Signal, while the second Hinfin algorithm uses the estimated Signal to update the parameters. In addition, as the Hinfin filter behavior depends on the choice of various weights, we present a way to recursively tune them. This approach is then studied in the following cases: (1) consistent estimation of the AR parameters from noisy observations and (2) speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyze the relevance of our solution.

David Labarre - One of the best experts on this subject based on the ideXlab platform.

  • Dual H infini Algorithms for Signal Processing, Application to Speech Enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Nicolai Christov, Mohamed Najim
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this prob-lem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual H filters. Once a new observation of the disturbed Signal is available, the first H algorithm uses the latest estimated parameters to estimate the Signal, while the second H algorithm uses the estimated Signal to update the parameters. In addition, as the H∞ filter behav-iour depends on the choice of various weights, we present a way to recursively tune them. This approach is then illustrated through the following cases: 1/ consistent estimation of the AR parameters from noisy observations, where the H scheme outperforms the existing algo-rithms; 2/ speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyse the relevance of our solution.

  • dual h _ infty algorithms for Signal Processing Application to speech enhancement
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: David Labarre, Eric Grivel, Mohamed Najim, Nicolai Christov
    Abstract:

    This paper deals with the joint Signal and parameter estimation for linear state-space models. An efficient solution to this problem can be obtained by using a recursive instrumental variable technique based on two dual Kalman filters. In that case, the driving process and the observation noise in the state-space representation for each filter must be white with known variances. These conditions, however, are too strong to be always satisfied in real cases. To relax them, we propose a new approach based on two dual Hinfin filters. Once a new observation of the disturbed Signal is available, the first Hinfin algorithm uses the latest estimated parameters to estimate the Signal, while the second Hinfin algorithm uses the estimated Signal to update the parameters. In addition, as the Hinfin filter behavior depends on the choice of various weights, we present a way to recursively tune them. This approach is then studied in the following cases: (1) consistent estimation of the AR parameters from noisy observations and (2) speech enhancement, where no a priori model of the additive noise is required for the proposed approach. In each case, a comparative study with existing methods is carried out to analyze the relevance of our solution.

Issa M S Panahi - One of the best experts on this subject based on the ideXlab platform.

  • from simulink to smartphone Signal Processing Application examples
    International Conference on Acoustics Speech and Signal Processing, 2015
    Co-Authors: Reza Pourrezashahri, Shane Parris, Fatemeh Saki, Issa M S Panahi, Nasser Kehtarnavaz
    Abstract:

    This paper presents the steps one needs to take in order to run a Signal Processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two Signal Processing Application examples from Simulink to smartphone. The Application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in Signal Processing, the approach presented in this paper is of benefit to practicing engineers and Signal Processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.

  • ICASSP - From Simulink to smartphone: Signal Processing Application examples
    2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015
    Co-Authors: Reza Pourreza-shahri, Shane Parris, Fatemeh Saki, Issa M S Panahi, Nasser Kehtarnavaz
    Abstract:

    This paper presents the steps one needs to take in order to run a Signal Processing algorithm designed in Simulink on the ARM processor of smartphones. The steps are conveyed by transitioning two Signal Processing Application examples from Simulink to smartphone. The Application examples involve background noise classification and lane departure detection. Considering that Simulink programming is widely used in Signal Processing, the approach presented in this paper is of benefit to practicing engineers and Signal Processing researchers/educators in terms of the process of making Simulink codes or models to run on smartphones.