Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909970
Oskar Keding, Maria Sandsten
This paper presents the Reassignment Vector Phase Difference Estimator (RVPDE), which gives noise robust relative phase estimates of oscillating transient signals in high noise levels. Estimation of relative phase information between signals is of interest for direction of arrival estimation, source separation and spatio-temporal decoding in neurology as well as for soundscape analysis. The RVPDE relies on the spectrogram reassignment vectors which contains information of the time-frequency local phase difference between two transient signals. The final estimate, which is robust to high noise levels, is given as the median over the local time-frequency area. The proposed technique is shown to outperform state-of-the-art methods in simulations for high noise levels. A discussion on the statistical distribution of the estimates is also presented, and finally an example of phase difference estimation of visually evoked potentials measured from electrical brain signals is shown.
{"title":"Robust Phase Difference Estimation of Transients in High Noise Levels","authors":"Oskar Keding, Maria Sandsten","doi":"10.23919/eusipco55093.2022.9909970","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909970","url":null,"abstract":"This paper presents the Reassignment Vector Phase Difference Estimator (RVPDE), which gives noise robust relative phase estimates of oscillating transient signals in high noise levels. Estimation of relative phase information between signals is of interest for direction of arrival estimation, source separation and spatio-temporal decoding in neurology as well as for soundscape analysis. The RVPDE relies on the spectrogram reassignment vectors which contains information of the time-frequency local phase difference between two transient signals. The final estimate, which is robust to high noise levels, is given as the median over the local time-frequency area. The proposed technique is shown to outperform state-of-the-art methods in simulations for high noise levels. A discussion on the statistical distribution of the estimates is also presented, and finally an example of phase difference estimation of visually evoked potentials measured from electrical brain signals is shown.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127754219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909712
T. Aizu, Ryo Matsuoka
When we take a photograph through glass windows or doors, the foreground scene is reflected in the captured image. The reflected components overlap with the background scene and make object recognition and identification more difficult. This paper proposes a novel reflection removal method using multiple polarized images taken with different exposure times. To achieve a high accuracy reflection removal in high dynamic range scenes, in which photographed images have under-/over-exposed pixels, we introduce a minimization problem of weighted nonnegative matrix factorization (WNMF) with total variation regularization. To solve this minimization problem, we also introduce an alternating optimization scheme with the alternating direction method of multipliers (AO-ADMM). The advantages of the proposed method over some conventional methods are demonstrated in experiments of reflection removal using real-world images.
{"title":"Reflection Removal Using Multiple Polarized Images with Different Exposure Times","authors":"T. Aizu, Ryo Matsuoka","doi":"10.23919/eusipco55093.2022.9909712","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909712","url":null,"abstract":"When we take a photograph through glass windows or doors, the foreground scene is reflected in the captured image. The reflected components overlap with the background scene and make object recognition and identification more difficult. This paper proposes a novel reflection removal method using multiple polarized images taken with different exposure times. To achieve a high accuracy reflection removal in high dynamic range scenes, in which photographed images have under-/over-exposed pixels, we introduce a minimization problem of weighted nonnegative matrix factorization (WNMF) with total variation regularization. To solve this minimization problem, we also introduce an alternating optimization scheme with the alternating direction method of multipliers (AO-ADMM). The advantages of the proposed method over some conventional methods are demonstrated in experiments of reflection removal using real-world images.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115933474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909602
Kai Kang, Qiyu Hu, Yunlong Cai, Guanding Yu, J. Hoydis, Y. Eldar
In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms.
{"title":"Joint Channel Estimation and Hybrid Beamforming via Deep-Unfolding","authors":"Kai Kang, Qiyu Hu, Yunlong Cai, Guanding Yu, J. Hoydis, Y. Eldar","doi":"10.23919/eusipco55093.2022.9909602","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909602","url":null,"abstract":"In this work, we propose an end-to-end deep-unfolding neural network (NN) based joint channel estimation and hybrid beamforming (JCEHB) algorithm to maximize the sum rate in massive multiple-input multiple-output (MIMO) systems. Specifically, the recursive least-squares (RLS) and stochastic successive convex approximation (SSCA) algorithms are unfolded for channel estimation and hybrid beamforming, respectively. We consider a mixed-timescale scheme, where analog beamforming matrices are designed based on the channel state information (CSI) statistics once in each frame, while the digital beamforming matrices are designed at each time slot based on the equivalent CSI matrices. Simulation results show that the proposed algorithm can significantly outperform conventional algorithms.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133458197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909651
Felix Vollmer, J. Grasshoff, P. Rostalski
Ultra-wideband (UWB) radio localization is a popular solution for indoor navigation. The time delay of radio signals between agents and anchors enables the inference of the agents' positions. The measurement of the time difference of arrival (TDoA) of these radio signals provides a scalable way to achieve localization. Due to factors like the antenna and room geometry TDoA measurements tend to contain a bias error. We present a probabilistic model-based approach to solve the TDoA localization problem with bias correction. By using stochastic variational Gaussian process (SVGP) regression with a tailored kernel we can exploit the problem structure and efficiently predict the measurement bias. Then we correct this bias by incorporating the Gaussian process (GP) predictions to a factor graph based localization scheme. The method is tested on data recorded from a quadrocopter and validated against an optical marker-based tracking. The framework manages to infer the location of the drone accurately and the proposed bias correction reduces localization errors significantly.
{"title":"Probabilistic Ultra-Wideband TDoA Localization with Bias Correction","authors":"Felix Vollmer, J. Grasshoff, P. Rostalski","doi":"10.23919/eusipco55093.2022.9909651","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909651","url":null,"abstract":"Ultra-wideband (UWB) radio localization is a popular solution for indoor navigation. The time delay of radio signals between agents and anchors enables the inference of the agents' positions. The measurement of the time difference of arrival (TDoA) of these radio signals provides a scalable way to achieve localization. Due to factors like the antenna and room geometry TDoA measurements tend to contain a bias error. We present a probabilistic model-based approach to solve the TDoA localization problem with bias correction. By using stochastic variational Gaussian process (SVGP) regression with a tailored kernel we can exploit the problem structure and efficiently predict the measurement bias. Then we correct this bias by incorporating the Gaussian process (GP) predictions to a factor graph based localization scheme. The method is tested on data recorded from a quadrocopter and validated against an optical marker-based tracking. The framework manages to infer the location of the drone accurately and the proposed bias correction reduces localization errors significantly.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132571073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909817
Ivan A. Vajs, V. Ković, Tamara Papić, A. Savić, M. Janković
Considering the negative impact dyslexia has on school achievements, dyslexia diagnosis and treatment are found to be of great importance. In this paper, a deep convolutional neural network was developed to detect dyslexia in children ages 7–13, based on gathered eye tracking data. The children read a text written in Serbian on 13 different color configurations (including background and overlay color variations) and the raw gaze coordinates gathered during the trials were formatted into colored images and used to train a deep learning model based on the VGG16 architecture. Several configurations of the convolutional neural network were evaluated, as well as several trial segmentation configurations in order to provide the best overall result. The method was evaluated using subject-wise cross-validation and an accuracy of 87% was achieved. The obtained results show that a combination of convolutional neural network and visual encoding of the eye tracking data shows promising results in dyslexia detection with minimal preprocessing.
{"title":"Dyslexia detection in children using eye tracking data based on VGG16 network","authors":"Ivan A. Vajs, V. Ković, Tamara Papić, A. Savić, M. Janković","doi":"10.23919/eusipco55093.2022.9909817","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909817","url":null,"abstract":"Considering the negative impact dyslexia has on school achievements, dyslexia diagnosis and treatment are found to be of great importance. In this paper, a deep convolutional neural network was developed to detect dyslexia in children ages 7–13, based on gathered eye tracking data. The children read a text written in Serbian on 13 different color configurations (including background and overlay color variations) and the raw gaze coordinates gathered during the trials were formatted into colored images and used to train a deep learning model based on the VGG16 architecture. Several configurations of the convolutional neural network were evaluated, as well as several trial segmentation configurations in order to provide the best overall result. The method was evaluated using subject-wise cross-validation and an accuracy of 87% was achieved. The obtained results show that a combination of convolutional neural network and visual encoding of the eye tracking data shows promising results in dyslexia detection with minimal preprocessing.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128903977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909635
S. Ntalampiras
The recent rise of adversarial machine learning highlights the vulnerabilities of various systems relevant in a wide range of application domains. This paper focuses on the important domain of automatic space surveillance based on the acoustic modality. After setting up a state of the art solution using log-Mel spectrogram modeled by a convolutional neural network, we systematically investigate the following four types of adversarial attacks: a) Fast Gradient Sign, b) Projected Gradient Descent, c) Jacobian Saliency Map, and d) Carlini & Wagner $ell_{infty}$. Experimental scenarios aiming at inducing false positives or negatives are considered, while attacks' efficiency are thoroughly examined. It is shown that several attack types are able to reach high success rate levels by injecting relatively small perturbations on the original audio signals. This underlines the need of suitable and effective defense strategies, which will boost reliability in machine learning based solution.
{"title":"Adversarial Attacks Against Audio Surveillance Systems","authors":"S. Ntalampiras","doi":"10.23919/eusipco55093.2022.9909635","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909635","url":null,"abstract":"The recent rise of adversarial machine learning highlights the vulnerabilities of various systems relevant in a wide range of application domains. This paper focuses on the important domain of automatic space surveillance based on the acoustic modality. After setting up a state of the art solution using log-Mel spectrogram modeled by a convolutional neural network, we systematically investigate the following four types of adversarial attacks: a) Fast Gradient Sign, b) Projected Gradient Descent, c) Jacobian Saliency Map, and d) Carlini & Wagner $ell_{infty}$. Experimental scenarios aiming at inducing false positives or negatives are considered, while attacks' efficiency are thoroughly examined. It is shown that several attack types are able to reach high success rate levels by injecting relatively small perturbations on the original audio signals. This underlines the need of suitable and effective defense strategies, which will boost reliability in machine learning based solution.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127657395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909843
Yuhao Liu, Marzieh Ajirak, P. Djurić
In this paper, we address the problem of sequential processing of observations modeled by deep Gaussian process state space models. First, we introduce the model where the Gaus-sian processes are based on random features and where both the transition and observation functions of the models are unknown. Then we propose a method that can estimate the unknowns of the model. The method allows for incremental learning of the system without requiring all the historical information. We also propose an ensemble version of the method, where each member of the ensemble has its own set of features. We show with computer simulations that the method can track the latent states up to scale and rotation.
{"title":"Inference with Deep Gaussian Process State Space Models","authors":"Yuhao Liu, Marzieh Ajirak, P. Djurić","doi":"10.23919/eusipco55093.2022.9909843","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909843","url":null,"abstract":"In this paper, we address the problem of sequential processing of observations modeled by deep Gaussian process state space models. First, we introduce the model where the Gaus-sian processes are based on random features and where both the transition and observation functions of the models are unknown. Then we propose a method that can estimate the unknowns of the model. The method allows for incremental learning of the system without requiring all the historical information. We also propose an ensemble version of the method, where each member of the ensemble has its own set of features. We show with computer simulations that the method can track the latent states up to scale and rotation.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131317256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909815
Florian Mouret, Mohanad Albughdadi, S. Duthoit, D. Kouamé, J. Tourneret
The Expectation-Maximization algorithm is a very popular approach for estimating the parameters of Gaussian mixture models (GMMs). A known issue with GMM estimation is its sensitivity to outliers, which can lead to poor estimation performance depending on the dataset under consideration. A common approach to deal with this issue is robust estimation, which typically consists of reducing the influence of the outliers on the estimators by weighting the impact of some samples of the dataset considered as outliers. In an unsupervised context, it is difficult to know which sample from the database corresponds to a normal observation. To that extent, we propose to use within the EM algorithm an outlier detection step that attributes an anomaly score to each sample of the database in an unsupervised way. A modified Bayesian Information Criterion is also introduced to efficiently select the appropriate amount of outliers contained in a dataset. The proposed method is tested on a benchmark remote sensing dataset coming from the UCI Machine Learning Repository. The experimental results show the interest of the proposed robustification when compared to other benchmark imputation procedures.
{"title":"Robust Estimation of Gaussian Mixture Models Using Anomaly Scores and Bayesian Information Criterion for Missing Value Imputation","authors":"Florian Mouret, Mohanad Albughdadi, S. Duthoit, D. Kouamé, J. Tourneret","doi":"10.23919/eusipco55093.2022.9909815","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909815","url":null,"abstract":"The Expectation-Maximization algorithm is a very popular approach for estimating the parameters of Gaussian mixture models (GMMs). A known issue with GMM estimation is its sensitivity to outliers, which can lead to poor estimation performance depending on the dataset under consideration. A common approach to deal with this issue is robust estimation, which typically consists of reducing the influence of the outliers on the estimators by weighting the impact of some samples of the dataset considered as outliers. In an unsupervised context, it is difficult to know which sample from the database corresponds to a normal observation. To that extent, we propose to use within the EM algorithm an outlier detection step that attributes an anomaly score to each sample of the database in an unsupervised way. A modified Bayesian Information Criterion is also introduced to efficiently select the appropriate amount of outliers contained in a dataset. The proposed method is tested on a benchmark remote sensing dataset coming from the UCI Machine Learning Repository. The experimental results show the interest of the proposed robustification when compared to other benchmark imputation procedures.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114807606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909695
Hassan Serhal, Nassib Abdallah, J. Marion, P. Chauvet, Mohamad Oueidat, A. Humeau-Heurtier
Prediction of atrial fibrillation (AF) is a major issue in medicine. This is due to the fact that AF is often asymptomatic. In this work, we present approaches based on wavelet decomposition to find features in the signal that can predict this disease. Our model consists of four parts: pre-processing, feature extraction, feature selection, and classification for prediction. The presented work shows a good predictive performance (94% accuracy) before 5 min of AF onset and a prediction accuracy of 85.5%, 110 min before AF onset. Our code will be available for researchers upon request.
{"title":"Wavelet transformation approaches for prediction of atrial fibrillation","authors":"Hassan Serhal, Nassib Abdallah, J. Marion, P. Chauvet, Mohamad Oueidat, A. Humeau-Heurtier","doi":"10.23919/eusipco55093.2022.9909695","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909695","url":null,"abstract":"Prediction of atrial fibrillation (AF) is a major issue in medicine. This is due to the fact that AF is often asymptomatic. In this work, we present approaches based on wavelet decomposition to find features in the signal that can predict this disease. Our model consists of four parts: pre-processing, feature extraction, feature selection, and classification for prediction. The presented work shows a good predictive performance (94% accuracy) before 5 min of AF onset and a prediction accuracy of 85.5%, 110 min before AF onset. Our code will be available for researchers upon request.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114387223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-08-29DOI: 10.23919/eusipco55093.2022.9909530
Tran Trong Duy, Ly V. Nguyen, V. Nguyen, N. Trung, K. Abed-Meraim
Fisher information is a fundamental quantity in information theory and signal processing. A direct analytical computation of the Fisher information is often infeasible or intractable due to the lack or sophistication of statistical models. In this paper, we propose a Fisher Information Neural Estimator (FINE) which is computationally efficient, highly accurate, and applicable for both cases of deterministic and random parame-ters. The proposed method solely depends on measured data and does not require knowledge or an estimate of the probability density function and is therefore universally applicable. We validate our approach using some experiments and compare with existing works. Numerical results show the high efficacy and low-computational complexity of the proposed estimation approach.
{"title":"Fisher Information Neural Estimation","authors":"Tran Trong Duy, Ly V. Nguyen, V. Nguyen, N. Trung, K. Abed-Meraim","doi":"10.23919/eusipco55093.2022.9909530","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909530","url":null,"abstract":"Fisher information is a fundamental quantity in information theory and signal processing. A direct analytical computation of the Fisher information is often infeasible or intractable due to the lack or sophistication of statistical models. In this paper, we propose a Fisher Information Neural Estimator (FINE) which is computationally efficient, highly accurate, and applicable for both cases of deterministic and random parame-ters. The proposed method solely depends on measured data and does not require knowledge or an estimate of the probability density function and is therefore universally applicable. We validate our approach using some experiments and compare with existing works. Numerical results show the high efficacy and low-computational complexity of the proposed estimation approach.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121975262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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