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prof. dr hab. inż. Maciej Niedźwiecki
Employment
- Professor at Department of Marine Electronic Systems
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total: 111
Catalog Publications
Year 2024
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On optimal tracking of rapidly varying telecommunication channels
PublicationWhen parameters of mobile telecommunication channels change rapidly, classical adaptive filters, such as exponentially weighted least squares algorithms or gradient algorithms, fail to estimate them with sufficient accuracy. In cases like this, one can use identification methods based on explicit models of parameter changes such as the method of basis functions (BF). When prior knowledge about parameter changes is available the...
Year 2023
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Fast Algorithms for Identification of Time-Varying Systems with Both Smooth and Discontinuous Parameter Changes
PublicationThe problem of noncausal identification of a time-varying linear system subject to both smooth and occasional jump-type changes is considered and solved using the preestimation technique combined with the basis function approach to modeling the variability of system parameters. The proposed estimation algorithms yield very good parameter tracking results and are computationally attractive.
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Karhunen-Loeve-based approach to tracking of rapidly fading wireless communication channels
PublicationWhen parameters of wireless communication channels vary at a fast rate, simple estimation algorithms, such as weighted least squares (WLS) or least mean squares (LMS) algorithms, cannot estimate them with the accuracy needed to secure the reliable operation of the underlying communication systems. In cases like this, the local basis function (LBF) estimation technique can be used instead, significantly increasing the achievable...
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On bidirectional preestimates and their application to identification of fast time-varying systems
PublicationWhen applied to the identification of time-varying systems, such as rapidly fading telecommunication channels, adaptive estimation algorithms built on the local basis function (LBF) principle yield excellent tracking performance but are computationally demanding. The subsequently proposed fast LBF (fLBF) algorithms, based on the preestimation principle, allow a substantial reduction in complexity without significant performance...
Year 2022
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Adaptive identification of sparse underwater acoustic channels with a mix of static and time-varying parameters
PublicationWe consider identification of sparse linear systems with a mix of static and time-varying parameters. Such systems are typical in underwater acoustics (UWA), for instance, in applications requiring identi- fication of the acoustic channel, such as UWA communications, navigation and continuous-wave sonar. The recently proposed fast local basis function (fLBF) algorithm provides high performance when identi- fying time-varying systems....
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Adaptive Identification of Underwater Acoustic Channel with a Mix of Static and Time-Varying Parameters
PublicationWe consider the problem of identification of communication channels with a mix of static and time-varying parameters. Such scenarios are typical, among others, in underwater acoustics. In this paper, we further develop adaptive algorithms built on the local basis function (LBF) principle resulting in excellent performance when identifying time-varying systems. The main drawback of an LBF algorithm is its high complexity. The subsequently...
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Finite-window RLS algorithms
PublicationTwo recursive least-squares (RLS) adaptive filtering algorithms are most often used in practice, the exponential and sliding (rectangular) window RLS algorithms. This popularity is mainly due to existence of low-complexity versions of these algorithms. However, these two windows are not always the best choice for identification of fast time-varying systems, when the identification performance is most important. In this paper, we...
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Optimally regularized local basis function approach to identification of time-varying systems
PublicationAccurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state of the art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately...
Year 2021
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Active Suppression of Nonstationary Narrowband Acoustic Disturbances
PublicationIn this chapter, a new approach to active narrowband noise control is presented. Narrowband acoustic noise may be generated, among others, by rotating parts of electro-mechanical devices, such as motors, turbines, compressors, or fans. Active noise control involves the generation of “antinoise”, i.e., the generation of a sound that has the same amplitude, but the opposite phase, as the unwanted noise, which causes them to interfere...
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Application of regularized Savitzky–Golay filters to identification of time-varying systems
PublicationSavitzky–Golay (SG) filtering is a classical signal smoothing technique based on the local least squares approximation of the analyzed signal by a linear combination of known functions of time (originally — powers of time, which corresponds to polynomial approximation). It is shown that the regularized version of the SG algorithm can be successfully applied to identification of time-varying finite impulse response (FIR) systems....
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Decoupled Kalman filter based identification of time-varying FIR systems
PublicationWhen system parameters vary at a fast rate, identification schemes based on model-free local estimation approaches do not yield satisfactory results. In cases like this, more sophisticated parameter tracking procedures must be used, based on explicit models of parameter variation (often referred to as hypermodels), either deterministic or stochastic. Kalman filter trackers, which belong to the second category, are seldom used in...
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Identification of Fast Time-varying Communication Channels Using the Preestimation Technique
PublicationAccurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately...
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Regularized identification of fast time-varying systems - comparison of two regularization strategies
PublicationThe problem of identification of a time-varying FIR system is considered and solved using the local basis function approach. It is shown that the estimation (tracking) results can be improved by means of regularization. Two variants of regularization are proposed and compared: the classical L2 (ridge) regularization and a new, reweighted L2 one. It is shown that the new approach can outperform the classical one and is computationally...
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Regularized Identification of Time-Varying FIR Systems Based on Generalized Cross-Validation
PublicationA new regularization method is proposed and applied to identification of time-varying finite impulse response systems. We show, that by a careful design of the regularization constraint, one can improve estimation results, especially in the presence of strong measurement noise. We also show that the the most appropriate regularization gain can be found by direct optimization of the generalized cross-validation criterion.
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Regularized Local Basis Function Approach to Identification of Nonstationary Processes
PublicationThe problem of identification of nonstationary stochastic processes (systems or signals) is considered and a new class of identification algorithms, combining the basis functions approach with local estimation technique, is described. Unlike the classical basis function estimation schemes, the proposed regularized local basis function estimators are not used to obtain interval approximations of the parameter trajectory, but provide...
Year 2020
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A new look at the statistical identification of nonstationary systems
PublicationThe paper presents a new, two-stage approach to identification of linear time-varying stochastic systems, based on the concepts of preestimation and postfiltering. The proposed preestimated parameter trajectories are unbiased but have large variability. Hence, to obtain reliable estimates of system parameters, the preestimated trajectories must be further filtered (postfiltered). It is shown how one can design and optimize such...
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A New Method of Noncausal Identification of Time-varying Systems
PublicationThe paper shows that the problem of noncausal identification of a time-varying FIR (finite impulse response) sys- tem can be reformulated, and solved, as a problem of smoothing of the preestimated parameter trajectories. Characteristics of the smoothing filter should be chosen so as to provide the best trade- off between the bias and variance of the resulting estimates. It is shown that optimization of the smoothing operation can...
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On the preestimation technique and its application to identification of nonstationary systems
PublicationThe problem of noncausal identification of a nonstationary stochastic FIR (finite impulse response) sys- tem is reformulated, and solved, as a problem of smoothing of preestimated parameter trajectories. Three approaches to preestimation are critically analyzed and compared. It is shown that optimization of the smoothing operation can be performed adaptively using the parallel estimation technique. The new approach is computationally...
Year 2019
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Fast Basis Function Estimators for Identification of Nonstationary Stochastic Processes
PublicationThe problem of identification of a linear nonsta-tionary stochastic process is considered and solved using theapproach based on functional series approximation of time-varying parameter trajectories. The proposed fast basis func-tion estimators are computationally attractive and yield resultsthat are better than those provided by the local least squaresalgorithms. It is shown that two...
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Fully Adaptive Savitzky-Golay Type Smoothers
PublicationThe problem of adaptive signal smoothing is consid-ered and solved using the weighted basis function approach. Inthe special case of polynomial basis and uniform weighting theproposed method reduces down to the celebrated Savitzky-Golaysmoother. Data adaptiveness is achieved via parallel estimation.It is shown that for the polynomial and harmonic bases andcosinusoidal weighting sequences, the competing signal estimatescan be computed...
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Generalized Savitzky–Golay filters for identification of nonstationary systems
PublicationThe problem of identification of nonstationary systems using noncausal estimation schemes is consid-ered and a new class of identification algorithms, combining the basis functions approach with localestimationtechnique,isdescribed.Unliketheclassicalbasisfunctionestimationschemes,theproposedlocal basis function estimators are not used to obtain interval approximations of the parametertrajectory, but provide a sequence of point...
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Lattice filter based multivariate autoregressive spectral estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a multivariate nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running...
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Local basis function estimators for identification of nonstationary systems
PublicationThe problem of identification of a nonstationary stochastic system is considered and solved using local basis function approximation of system parameter trajectories. Unlike the classical basis function approach, which yields parameter estimates in the entire analysis interval, the proposed new identification procedure is operated in a sliding window mode and provides a sequence of point (rather than interval) estimates. It is...
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On Adaptive Spectrum Estimation of Multivariate Autoregressive Locally Stationary Processes
PublicationAutoregressive modeling is a widespread parametricspectrum estimation method. It is well known that, in the caseof stationary processes with unknown order, its accuracy canbe improved by averaging models of different complexity usingsuitably chosen weights. The paper proposes an extension of thistechnique to the case of multivariate locally stationary processes.The proposed solution is based on local autoregressive...
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On Noncausal Identification of Nonstationary Multivariate Autoregressive Processes
PublicationThe problem of identification of nonstationary multivariate autoregressive processes using noncausal local estimation schemes is considered and a new approach to joint selection of the model order and the estimation bandwidth is proposed. The new selection rule, based on evaluation of pseudoprediction errors, is compared with the previously proposed one, based on the modified Akaike’s final prediction error criterion.
Year 2018
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Identification of nonstationary multivariate autoregressive processes– Comparison of competitive and collaborative strategies for joint selection of estimation bandwidth and model order
PublicationThe problem of identification of multivariate autoregressive processes (systems or signals) with unknown and possibly time-varying model order and time-varying rate of parameter variation is considered and solved using parallel estimation approach. Under this approach, several local estimation algorithms, with different order and bandwidth settings, are run simultaneously and compared based on their predictive performance. First,...
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Identification of nonstationary processes using noncausal bidirectional lattice filtering
PublicationThe problem of off-line identification of a nonstationary autoregressive process with a time-varying order and a time-varying degree of nonstationarity is considered and solved using the parallel estimation approach. The proposed parallel estimation scheme is made up of several bidirectional (noncausal) exponentially weighted lattice algorithms with different estimation memory and order settings. It is shown that optimization of...
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New results on estimation bandwidth adaptation
PublicationThe problem of identification of a nonstationary autoregressive signal using non-causal estimation schemes is considered. Noncausal estimators can be used in applications that are not time-critical, i.e., do not require real-time processing. A new adaptive estimation bandwidth selection rule based on evaluation of pseudoprediction errors is proposed, allowing one to adjust tracking characteristics of noncausal estimators to unknown...
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Two-Stage Identification of Locally Stationary Autoregressive Processes and its Application to the Parametric Spectrum Estimation
PublicationThe problem of identification of a nonstationary autoregressive process with unknown, and possibly time-varying, rate of parameter changes, is considered and solved using the parallel estimation approach. The proposed two-stage estimation scheme, which combines the local estimation approach with the basis function one, offers both quantitative and qualitative improvements compared with the currently used single-stage methods.
Year 2017
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Akaike's final prediction error criterion revisited
PublicationWhen local identification of a nonstationary ARX system is carried out, two important decisions must be taken. First, one should decide upon the number of estimated parameters, i.e., on the model order. Second, one should choose the appropriate estimation bandwidth, related to the (effective) number of input-output data samples that will be used for identification/ tracking purposes. Failure to make the right decisions results...
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Detection of impulsive disturbances in archive audio signals
PublicationIn this paper the problem of detection of impulsive disturbances in archive audio signals is considered. It is shown that semi-causal/noncausal solutions based on joint evaluation of signal prediction errors and leave-one-out signal interpolation errors, allow one to noticeably improve detection results compared to the prediction-only based solutions. The proposed approaches are evaluated on a set of clean audio signals contaminated...
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Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation
PublicationThe problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in...
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New semi-causal and noncausal techniques for detection of impulsive disturbances in multivariate signals with audio applications
PublicationThis paper deals with the problem of localization of impulsive disturbances in nonstationary multivariate signals. Both unidirectional and bidirectional (noncausal) detection schemes are proposed. It is shown that the strengthened pulse detection rule, which combines analysis of one-step-ahead signal prediction errors with critical evaluation of leave-one-out signal interpolation errors, allows one to noticeably improve detection results...
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On adaptive covariance and spectrum estimation of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one has to make two important decisions. First, one should choose the so-called estimation bandwidth, inversely proportional to the effective width of the local analysis window, in the way that complies with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive...
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On autoregressive spectrum estimation using the model averaging technique
PublicationThe problem of estimating spectral density of a nonstationary process satisfying local stationarity conditions is considered. The proposed solution is a two step procedure based on local autoregressive (AR) modeling. In the first step Bayesian-like averaging of AR models, differing in order, is performed. The main contribution of the paper is development of a new final-prediction-error-like statistic, which can be used to select...
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On joint order and bandwidth selection for identification of nonstationary autoregressive processes
PublicationWhen identifying a nonstationary autoregressive process, e.g. for the purpose of signal prediction or parametric spectrum estimation, two important decisions must be taken. First, one should choose the appropriate order of the autoregressive model, i.e., the number of autoregressive coefficients that will be estimated. Second, if identification is carried out using the local estimation technique, such as the localized version of...
Year 2016
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High-Precision FIR-Model-Based Dynamic Weighing System
PublicationConveyor belt-type checkweighers are increasingly popular components of modern production lines. They are used to assess the weight of the produced items in motion, i.e., without stopping them on the weighing platform. The main challenge one faces when designing a dynamic weighing system is providing high measurement accuracy, especially at high conveyor belt speeds. The approach proposed in this paper can be characterized as a...
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On adaptive selection of estimation bandwidth for analysis of locally stationary multivariate processes
PublicationWhen estimating the correlation/spectral structure of a locally stationary process, one should choose the so-called estimation bandwidth, related to the effective width of the local analysis window. The choice should comply with the degree of signal nonstationarity. Too small bandwidth may result in an excessive estimation bias, while too large bandwidth may cause excessive estimation variance. The paper presents a novel method...
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Systemidentificationbasedapproachtodynamicweighing revisited
PublicationDynamicweighing,i.e.,weighingofobjectsinmotion,withoutstoppingthemonthe weighing platform,allowsonetoincreasetherateofoperationofautomaticweighing systems, usedinindustrialproductionprocesses,withoutcompromisingtheiraccuracy. Sincetheclassicalidentification-basedapproachtodynamicweighing,basedonthe second-ordermass–spring–dampermodeloftheweighingsystem,doesnotyieldsa- tisfactoryresultswhenappliedtoconveyorbelttypecheckweighers,severalextensionsof thistechniqueareexamined.Experimentsconfirmthatwhenappropriatelymodifiedthe identification-basedapproachbecomesareliabletoolfordynamicmassmeasurementin checkweighers.
Year 2015
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Active feedback noise control in the presence of impulsive disturbances
PublicationThe problem of active feedback control of a narrowband acoustic noise in the presence of impulsive disturbances is considered. It is shown that, when integrated with appropriately designed outlier detector, the proposed earlier feedback control algorithm called SONIC is capable of isolating and rejecting noise pulses. According to our tests this guarantees stable and reliable operation of the closed-loop noise cancelling...
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Automated Detection of Sleep Apnea and Hypopnea Events Based on Robust Airflow Envelope Tracking in the Presence of Breathing Artifacts. - [IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS]
PublicationThe paper presents a new approach to detection of apnea/hypopnea events, in the presence of artifacts and breathing irregularities, from a single channel airflow record. The proposed algorithm, based on a robust envelope detector , identifies segments of signal affected by a high amplitude mo d- ulation corresponding to apnea/hypopnea events. It is show n that a robust airflow envelope - free of breathing artifacts - improves effectiveness...
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Elimination of Impulsive Disturbances From Stereo Audio Recordings Using Vector Autoregressive Modeling and Variable-order Kalman Filtering
PublicationThis paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. Online tracking of signal model parameters is performed using the exponential ly weighted least squares algo- rithm. Detection of noise pulses an d model-based interpolation of the irrevocably distorted sampl es is realized using an adaptive, variable-order...
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Localization of impulsive disturbances in audio signals using template matching
PublicationIn this paper, a new solution to the problem of elimination of impulsive disturbances from audio signals, based on the matched filtering technique, is proposed. The new approach stems from the observation that a large proportion of noise pulses corrupting audio recordings have highly repetitive shapes that match several typical “patterns”. In many cases a representative set of exemplary pulse waveforms can be extracted from the...
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Robust algorithm for active feedback control of narrowband noise
PublicationThe problem of active control of narrowband acoustic noise is considered. It is shown that the proposed earlier feedback control algorithm called SONIC (self-optimizing narrowband interference canceller), based on minimization of the L2-norm performance measure, can be re-derived using the L1 approach. The resulting robust SONIC algorithm is more robust to heavy-tailed measurement noise, such as the αlpha-stable noise, than the...
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Sparse vector autoregressive modeling of audio signals and its application to the elimination of impulsive disturbances
PublicationArchive audio files are often corrupted by impulsive disturbances, such as clicks, pops and record scratches. This paper presents a new method for elimination of impulsive disturbances from stereo audio signals. The proposed approach is based on a sparse vector autoregressive signal model, made up of two components: one taking care of short-term signal correlations, and the other one taking care of long-term correlations. The method...
Year 2014
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Adaptive filtering approach to dynamic weighing: a checkweigher case study
PublicationDynamic weighing, i.e., weighing of objects in motion, with out stopping them on the weighing platform, allows one to increase the rate of operation of automatic weighing systems used in industrial production processes without compromising their accuracy. The paper extends and compares two approaches to dynamic weighing, based on system identification and variable-bandwidth filtering, respectively. Experiments, carried on a conveyor...
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Automated detection of sleep apnea and hypopnea events based on robust airflow envelope tracking
PublicationThe paper presents a new approach to detection of apnea/hypopnea events, in the presence of artifacts and breathing irregularities, from a single-channel airflow record. The proposed algorithm identifies segments of signal affected by a high amplitude modulation corresponding to apnea/hypopnea events. It is shown that a robust airflow envelope—free of breathing artifacts—improves effectiveness of the diagnostic process and allows...
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Dynamic mass measurement in checkweighers using a discrete time-variant low-pass filter
PublicationConveyor belt type checkweighers are complex mechanical systems consisting of a weighing sensor (strain gauge load cell, electrodynamically compensated load cell), packages (of different shapes, made of different materials) and a transport system (motors, gears, rollers). Disturbances generated by the vibrating parts of such a system are reflected in the signal power spectra in a form of strong spectral peaks, located usually in...
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Elimination of impulsive disturbances from archive audio files – comparison of three noise pulse detection schemes
PublicationThe problem of elimination of impulsive disturbances (such as clicks, pops, ticks, crackles, and record scratches) from archive audio recordings is considered and solved using autoregressive modeling. Three classical noise pulse detection schemes are examined and compared: the approach based on open-loop multi-step-ahead signal prediction, the approach based on decision-feedback signal prediction, and the double threshold approach,...
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Elimination of impulsive disturbances from stereo audio recordings
PublicationThis paper presents a new approach to elimination of impulsive disturbances from stereo audio recordings. The proposed solution is based on vector autoregressive modeling of audio signals. On-line tracking of signal model parameters is performed using the stability-preserving Whittle-Wiggins-Robinson algorithm with exponential data weighting. Detection of noise pulses and model-based interpolation of the irrevocably distorted samples...
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