Robust smoothing of one‐dimensional data with missing and/or outlier values

Mourad, Nasser

doi:10.1049/sil2.12033

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…(7) The optimum values of IMAR model parameters are estimated via maximum likelihood estimation. Equations ( 3) and estimate the parameter values of the source spectral component vectors (4).…”

Section: Fig: 3 Representation Of Imar Modelmentioning

confidence: 99%

IMAR in Frequency Domain for Separating Speech Signals by STFT

C Anna Palagan

2024

jes

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Blind Source Separation is a statistical method for isolating signals coming from unidentified sources that have been picked up by a variety of sensors. In the fields of biological signal analysis and speech analysis technology, it is an active researcher. The quantity of noise that is present in the signal throughout the speech signal separation process is a disadvantage. This noise affects the signal that has been separated, and it is often loud melodic noise. For the purpose of separating speech signals from BSS, the suggested method makes use of a network that is configured with the parameters of Instantaneous Mixing in addition to an Auto Regressive model and a maximum-likelihood function. In order to separate the individual voice signal from the input mixed signals, the Back Propagation Network is put into operation. The Short Time Fourier Transform is then utilized for the purpose of dividing the spoken stream into extremely precise time periods. Next, Maximum-likelihood method is implemented to determine the optimum values of the IMAR model's W and G parameters. This step is done after the ideal values have been estimated. The suggested model was tested using a combination of voice signals and microphone signals, and the results demonstrated that it provided superior execution in comparison to other algorithms that are currently in use.

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Section: Fig: 3 Representation Of Imar Modelmentioning

confidence: 99%

IMAR in Frequency Domain for Separating Speech Signals by STFT

C Anna Palagan

2024

jes

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“…Such random changes in amplitude can negatively impact application-level performance. Furthermore, during measurements, the signal is susceptible to outlier samples caused by external factors such as instrument malfunction [70]. Signal smoothing attempts to remove such impairments in the signal by adjusting the amplitudes of individual samples with respect to the amplitudes of adjacent samples.…”

Section: A Preprocessingmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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“…The lackness of chemical contents data can be filled with some substituted values, known as data imputation. In the past few decades, many data-driven methods have been proposed to address the imputation of missing values in industrial and engineering problems [5][6][7][8][9]. The most simple solutions are to impute the missing values with some statistical values, such as mean, median, and the last observation [10].…”

Section: Introductionmentioning

confidence: 99%

Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials

Liu,

Chen,

et al. 2023

IET Signal Processing

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Chemical contents, the important quality indicators are crucial for the modeling of sintering process. However, the lack of these data can result in the biasedness of state estimation in sintering process. It, thus, greatly reduces the accuracy of modeling. Although there are some general imputation methods to tackle the data lackness, they rarely consider the interoutputs correlation and the negative impacts caused by incorrect prefilling. In this article, a novel sparse multioutput Gaussian convolution process (MGCP) modeling framework is proposed for data imputation. MGCP can flexibly mine the relationships between the outputs by a convolution of a sharing latent function and different Gaussian kernels. Moreover, the penalization terms are designed to weaken the false relationship between these outputs. To generalize the MGCP to a long-period case, dynamic time warping term is introduced to keep the global similarity between the original and estimated time series. Compared with several existing methods, the proposed method shows great superiority in sintering raw material contents estimation with real-world data.

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Robust smoothing of one‐dimensional data with missing and/or outlier values

Cited by 7 publications

References 33 publications

IMAR in Frequency Domain for Separating Speech Signals by STFT

IMAR in Frequency Domain for Separating Speech Signals by STFT

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Regularized Multioutput Gaussian Convolution Process for Chemical Contents Data Imputation in Sintering Raw Materials

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