A periodic Levinson–Durbin algorithm for entropy maximization

Boshnakov, Georgi N.; Lambert-Lacroix, Sophie

doi:10.1016/j.csda.2011.07.001

Cited by 15 publications

(8 citation statements)

References 9 publications

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“…Least mean square (LMS) and normalized least mean square (NLMS) algorithms in the cyclostationary regime are analyzed in [25]. A periodic Levinson-Durbin algorithm for entropy maximization is derived in [35].…”

Section: Mmse Linear Filteringmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

175

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Section: Mmse Linear Filteringmentioning

confidence: 99%

Cyclostationarity: New trends and applications

Napolitano

2016

Signal Processing

175

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“…The linear predictor coefficients can be calculated by using the Levinson-Durbin algorithm [34]. If we assume that speckle noise is a kind of additive noise, we can write the noisy signal as:…”

Section: Two-sided Linear Predictormentioning

confidence: 99%

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

Wang

Zhang

Wang

2021

IEICE Trans. Inf. & Syst.

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Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

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“…In recent studies, Lee and Fu [26] applied the entropy maximization model to population synthesis in an activity-based microsimulation model and used a quasi-Newton algorithm to solve a large number of dimensions. Boshnakov and Lambert-Lacroix [27] proposed a periodic Levinson-Durbin algorithm, the implementation of which was available with the R package. To solve the entropy maximization model, Li et al [28] introduced a hybrid intelligent algorithm that assumed (1) the travel costs per unit that ows between di erent zones are fuzzy variables, and (2) trip productions and attractions are random variables.…”

Section: Literature Reviewmentioning

confidence: 99%

Tour-Based Truck Demand Modeling with Entropy Maximization Using GPS Data

You

Ritchie

2019

Journal of Advanced Transportation

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An approach is presented to determine the most likely tour distributions and model behavior for investigating drayage truck movements in a coastal region. This was done by implementing a revised form of entropy maximization based on truck tours to model and better understand drayage truck tour behavior at the San Pedro Bay Ports (SPBPs) complex in Southern California. The drayage trucks at the SPBPs have features that are distinct from other commercial trucks. The tour-based entropy maximization model proposed in this paper provides an opportunity to incorporate periodically updated GPS data collected in Southern California into a large-scale tour-based model. With the dataset, four models were estimated by cargo movement: (1) year-based, (2) low period, (3) medium period, and (4) high period models. The findings were consistent with the tour patterns varying by season and by cargo movement. Furthermore, the medium period, which represented relatively steady cargo movement, indicated a better MAPE (mean absolute percent error) than did other models. This proposed approach provides a significant advantage in that the most recent touring information obtained from advanced technologies could be directly applied to the tour-based model and subsequently used to assess various strategies.

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A periodic Levinson–Durbin algorithm for entropy maximization

Cited by 15 publications

References 9 publications

Cyclostationarity: New trends and applications

Cyclostationarity: New trends and applications

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

Tour-Based Truck Demand Modeling with Entropy Maximization Using GPS Data

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