Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter

Kazemi, Reza; Farsi, A.; Ghaed, Mohammad Hassan; Karimi-Ghartemani, Masoud

doi:10.1016/j.sigpro.2008.02.012

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…To overcome this problem numerous approaches have been proposed in the last few decades. For example, the Kalman filter and the Wiener filter, Recursive-Least-Square (RLS) algorithm, were proposed to achieve the optimum performance of adaptive filters [10][11][12]. Amongst these the Least Mean Square (LMS) algorithm is most frequently used because of its simplicity and robustness.…”

Section: Introductionmentioning

confidence: 99%

LMS Adaptive Filters for Noise Cancellation: A Review

Dixit¹,

Nagaria²

2017

IJECE

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This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted.

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Section: Introductionmentioning

confidence: 99%

LMS Adaptive Filters for Noise Cancellation: A Review

Dixit¹,

Nagaria²

2017

IJECE

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“…The reference estimation procedure in this context is the Kalman filter, which is widely used in many applications, and in particular in biomedical signal processing [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A measurement policy in stochastic linear filtering problems

Bruni

Koch

Papa

2011

Computers & Mathematics with Applications

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a b s t r a c tWith reference to a (discrete time) linear system filtering problem, we consider the problem of online deciding at which instant we actually measure and process the output values. We propose a convenient measurement policy, able to sensibly reduce the measurement cost, while keeping the estimate accuracy at satisfactory levels. By some probabilistic arguments we analyse the upper bounds for the measurement and nonmeasurement time intervals.

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“…To estimate the model parameters they use a modified version of the Kalman Filter, the Extended Kalman Filter (EKF) (Haykin, 2001). In (Kazemi et al, 2008) the authors use the Kalman Filter to detect and extract periodic noise from the ECG. In (Georgiadis et al, 2005;Georgiadis et al, 2007) they assumed that the Evoked Potentials in the Electroencephalogram can be represented as a linear combination of basis functions.…”

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confidence: 99%

The Use of Kalman Filter in Biomedical Signal Processing

Oikonomou¹,

Tzallas²,

Konitsiotis³

et al. 2009

Kalman Filter Recent Advances and Applications

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The Kalman Filter (KF) is a powerful tool in the analysis of the evolution of a dynamical model in time. The filter provides with a flexible manner to obtain recursive estimation of the parameters, which are optimal in the mean square error sense. The properties of KF along with the simplicity of the derived equations make it valuable in the analysis of signals. In this chapter an overview of the Kalman Filter, its properties and its applications is presented. More specifically, we focus on the application of Kalman Filter in the Electroencephalogram (EEG) processing, addressing extensions of Kalman Filter such as the Kalman Smoother (KS) in the time varying autoregressive (TVAR) model. The model can be written in a state-space form and the employment of KF provides with an estimation of the AR parameters which can be used for the estimation of the non-stationary signal. It is also demonstrated how these parameters can be used as input features of the signal in a clustering approach. The Kalman Filter is an estimator with interesting properties like optimality in the Minimum Mean Square Error (MMSE). After its discovery in 1960 (Kalman, 1960), this estimator has been used in many fields of engineering such as control theory, communication systems, speech processing, biomedical signal processing, etc. An analogous estimator has been proposed for the smoothing problem (Rauch et al., 1963), which includes three different types of smoothers, namely fixed-lag, fixed-point and fixed interval (Anderson & Moore, 1979; Brown, 1983). In this chapter we address the fixed interval smoother. The difference between the two estimators, the Kalman Filter and the Kalman Smoother, it is related on how they use the observations to perform estimation. The Kalman Filter uses only the past and the present observations to perform estimation, while the Kalman Smoother uses also the future observations for the estimation. This means that the Kalman Filter is used for on-line processing while the Kalman Smoother for batch processing. The derivations of these two estimators is presented in (Kay, 1993; Grewal & Andrews, 2001; Haykin, 2001). Both estimators are recursive in nature. This means that the estimate of the present state is updated using the previous state only and not the entire past states. The Kalman Filter is not only an estimator but also a learning method (Grewal & Andrews, 2001; Bishop, 2006). The observations are used to learn the states of the model. The Kalman Filter is also a computational tool and some problems may exist due to the finite precision arithmetic of the computers.

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Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter

Cited by 10 publications

References 8 publications

LMS Adaptive Filters for Noise Cancellation: A Review

LMS Adaptive Filters for Noise Cancellation: A Review

A measurement policy in stochastic linear filtering problems

The Use of Kalman Filter in Biomedical Signal Processing

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