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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Video-Driven Speech Reconstruction - Show & Tell Demo


This demo will showcase our video-to-audio model which attempts to reconstruct speech from short videos of spoken statements. Our model does so in a completely end-to-end manner where raw audio is generated based on the input video. This approach bypasses the need for separate lip-reading and text-to-speech models. The advantage of such an approach is that it does not require large transcribed datasets and it is not based on intermediate representations like text which remove any intonation and emotional content from the speech.

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Authors:
Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic
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15 May 2020 - 11:55am
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Video-driven Speech Reconstruction using Generative Adversarial Networks Show & Tell Demo.pdf

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[1] Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic, "Video-Driven Speech Reconstruction - Show & Tell Demo", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5351. Accessed: Jul. 14, 2020.
@article{5351-20,
url = {http://sigport.org/5351},
author = {Konstantinos Vougioukas; Stavros Petridis; Björn Schuller; Maja Pantic },
publisher = {IEEE SigPort},
title = {Video-Driven Speech Reconstruction - Show & Tell Demo},
year = {2020} }
TY - EJOUR
T1 - Video-Driven Speech Reconstruction - Show & Tell Demo
AU - Konstantinos Vougioukas; Stavros Petridis; Björn Schuller; Maja Pantic
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5351
ER -
Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic. (2020). Video-Driven Speech Reconstruction - Show & Tell Demo. IEEE SigPort. http://sigport.org/5351
Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic, 2020. Video-Driven Speech Reconstruction - Show & Tell Demo. Available at: http://sigport.org/5351.
Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic. (2020). "Video-Driven Speech Reconstruction - Show & Tell Demo." Web.
1. Konstantinos Vougioukas, Stavros Petridis, Björn Schuller, Maja Pantic. Video-Driven Speech Reconstruction - Show & Tell Demo [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5351

IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS


Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under cross-dataset evaluation the performance of these PAD systems drops significantly. This lack of generalization is attributed to domain-shift. Here, we propose a novel PAD method that leverages the large variability present in FR datasets to induce invariance to factors that cause domain-shift.

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Authors:
Sushil Bhattacharjee, Sebastien Marcel
Submitted On:
15 May 2020 - 10:22am
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autoencoder_error_icassp_2020_slides.pdf

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[1] Sushil Bhattacharjee, Sebastien Marcel, "IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5350. Accessed: Jul. 14, 2020.
@article{5350-20,
url = {http://sigport.org/5350},
author = {Sushil Bhattacharjee; Sebastien Marcel },
publisher = {IEEE SigPort},
title = {IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS},
year = {2020} }
TY - EJOUR
T1 - IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS
AU - Sushil Bhattacharjee; Sebastien Marcel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5350
ER -
Sushil Bhattacharjee, Sebastien Marcel. (2020). IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS. IEEE SigPort. http://sigport.org/5350
Sushil Bhattacharjee, Sebastien Marcel, 2020. IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS. Available at: http://sigport.org/5350.
Sushil Bhattacharjee, Sebastien Marcel. (2020). "IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS." Web.
1. Sushil Bhattacharjee, Sebastien Marcel. IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5350

DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION


With face-recognition (FR) increasingly replacing fingerprint sensors for user-authentication on mobile devices, presentation attacks (PA) have emerged as the single most significant hurdle for manufacturers of FR systems. Current machine-learning based presentation attack detection (PAD) systems, trained in a data-driven fashion, show excellent performance when evaluated in intra-dataset scenarios. Their performance typically degrades significantly in cross-dataset evaluations. This lack of generalization in current PAD systems makes them unsuitable for deployment in real-world scenarios.

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Authors:
Sushil Bhattacharjee, Sebastien Marcel
Submitted On:
15 May 2020 - 10:19am
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domain_guided_pruning_icassp_2020_slides.pdf

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[1] Sushil Bhattacharjee, Sebastien Marcel, "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5349. Accessed: Jul. 14, 2020.
@article{5349-20,
url = {http://sigport.org/5349},
author = {Sushil Bhattacharjee; Sebastien Marcel },
publisher = {IEEE SigPort},
title = {DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION},
year = {2020} }
TY - EJOUR
T1 - DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION
AU - Sushil Bhattacharjee; Sebastien Marcel
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5349
ER -
Sushil Bhattacharjee, Sebastien Marcel. (2020). DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. IEEE SigPort. http://sigport.org/5349
Sushil Bhattacharjee, Sebastien Marcel, 2020. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION. Available at: http://sigport.org/5349.
Sushil Bhattacharjee, Sebastien Marcel. (2020). "DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION." Web.
1. Sushil Bhattacharjee, Sebastien Marcel. DOMAIN ADAPTATION FOR GENERALIZATION OF FACE PRESENTATION ATTACK DETECTION IN MOBILE SETTINGS WITH MINIMAL INFORMATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5349

Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System


Conventional Computed Tomography (CT) systems use a single X-ray source and an arc of detectors mounted on a rotating gantry to acquire a set of projection data. Novel CT systems are now being pioneered in which a complete ring of distributed X-ray sources and detectors are electronically turned on and off, without any mechanical motion, to acquire a set of projections for tomographic reconstruction. This paper discusses new sensing and reconstruction paradigms enabled by this new CT architecture.

Paper Details

Authors:
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta
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15 May 2020 - 10:05am
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PDF of presentation

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[1] Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta, "Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5348. Accessed: Jul. 14, 2020.
@article{5348-20,
url = {http://sigport.org/5348},
author = {Alankar Kowtal; Avilash Cramer; Dufan Wu; Kai Yang; Wolfgang Krull; Ioannis Gkioulekas; Rajiv Gupta },
publisher = {IEEE SigPort},
title = {Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System},
year = {2020} }
TY - EJOUR
T1 - Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System
AU - Alankar Kowtal; Avilash Cramer; Dufan Wu; Kai Yang; Wolfgang Krull; Ioannis Gkioulekas; Rajiv Gupta
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5348
ER -
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. (2020). Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System. IEEE SigPort. http://sigport.org/5348
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta, 2020. Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System. Available at: http://sigport.org/5348.
Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. (2020). "Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System." Web.
1. Alankar Kowtal, Avilash Cramer, Dufan Wu, Kai Yang, Wolfgang Krull, Ioannis Gkioulekas, Rajiv Gupta. Signal Sensing and Reconstruction Paradigms for a Novel Multi-source Static Computed Tomography System [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5348

ICASSP 2020 Presentation Poster Slides


ONE-SHOT VOICE CONVERSION USING STAR-GAN

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15 May 2020 - 9:05am
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[1] , "ICASSP 2020 Presentation Poster Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5347. Accessed: Jul. 14, 2020.
@article{5347-20,
url = {http://sigport.org/5347},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP 2020 Presentation Poster Slides},
year = {2020} }
TY - EJOUR
T1 - ICASSP 2020 Presentation Poster Slides
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5347
ER -
. (2020). ICASSP 2020 Presentation Poster Slides. IEEE SigPort. http://sigport.org/5347
, 2020. ICASSP 2020 Presentation Poster Slides. Available at: http://sigport.org/5347.
. (2020). "ICASSP 2020 Presentation Poster Slides." Web.
1. . ICASSP 2020 Presentation Poster Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5347

Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems

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Authors:
S. H. Mirfarshbafan and C. Studer
Submitted On:
15 May 2020 - 6:23am
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20ICASSP_beamspars.pdf

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[1] S. H. Mirfarshbafan and C. Studer, "Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5346. Accessed: Jul. 14, 2020.
@article{5346-20,
url = {http://sigport.org/5346},
author = {S. H. Mirfarshbafan and C. Studer },
publisher = {IEEE SigPort},
title = {Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems},
year = {2020} }
TY - EJOUR
T1 - Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems
AU - S. H. Mirfarshbafan and C. Studer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5346
ER -
S. H. Mirfarshbafan and C. Studer. (2020). Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems. IEEE SigPort. http://sigport.org/5346
S. H. Mirfarshbafan and C. Studer, 2020. Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems. Available at: http://sigport.org/5346.
S. H. Mirfarshbafan and C. Studer. (2020). "Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems." Web.
1. S. H. Mirfarshbafan and C. Studer. Sparse Beamspace Equalization for Massive MU-MIMO mmWave Systems [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5346

DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement


Multi-frame approaches for single-microphone speech enhancement, e.g., the multi-frame minimum-power-distortionless-response (MFMPDR) filter, are able to exploit speech correlations across neighboring time frames. In contrast to single-frame approaches such as the Wiener gain, it has been shown that multi-frame approaches achieve a substantial noise reduction with hardly any speech distortion, provided that an accurate estimate of the correlation matrices and especially the speech interframe correlation (IFC) vector is available.

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Authors:
Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo
Submitted On:
15 May 2020 - 6:12am
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ICASSP2020_Tammenetal.pdf

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[1] Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo, "DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5345. Accessed: Jul. 14, 2020.
@article{5345-20,
url = {http://sigport.org/5345},
author = {Marvin Tammen; Dörte Fischer; Bernd T. Meyer; Simon Doclo },
publisher = {IEEE SigPort},
title = {DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement},
year = {2020} }
TY - EJOUR
T1 - DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement
AU - Marvin Tammen; Dörte Fischer; Bernd T. Meyer; Simon Doclo
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5345
ER -
Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo. (2020). DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement. IEEE SigPort. http://sigport.org/5345
Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo, 2020. DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement. Available at: http://sigport.org/5345.
Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo. (2020). "DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement." Web.
1. Marvin Tammen, Dörte Fischer, Bernd T. Meyer, Simon Doclo. DNN-Based Speech Presence Probability Estimation for Multi-Frame Single-Microphone Speech Enhancement [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5345

SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION


Non-intrusive load monitoring (a.k.a. power disaggregation) refers to identifying and extracting the consumption patterns of individual appliances from the mains which records the whole-house energy consumption. Recently, deep learning has been shown to be a promising method to solve this problem and many approaches based on it have been proposed.

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Authors:
Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia
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15 May 2020 - 5:10am
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SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION.pdf

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[1] Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia, "SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5344. Accessed: Jul. 14, 2020.
@article{5344-20,
url = {http://sigport.org/5344},
author = {Yungang Pan; Ke Liu; Zhaoyan Shen; Xiaojun Cai; Zhiping Jia },
publisher = {IEEE SigPort},
title = {SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION},
year = {2020} }
TY - EJOUR
T1 - SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION
AU - Yungang Pan; Ke Liu; Zhaoyan Shen; Xiaojun Cai; Zhiping Jia
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5344
ER -
Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia. (2020). SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION. IEEE SigPort. http://sigport.org/5344
Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia, 2020. SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION. Available at: http://sigport.org/5344.
Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia. (2020). "SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION." Web.
1. Yungang Pan, Ke Liu, Zhaoyan Shen, Xiaojun Cai, Zhiping Jia. SEQUENCE-TO-SUBSEQUENCE LEARNING WITH CONDITIONAL GAN FOR POWER DISAGGREGATION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5344

Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification

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Authors:
Yuzhong Wu, Tan Lee
Submitted On:
15 May 2020 - 5:07am
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icassp2020_ppt_yzwu.pdf

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[1] Yuzhong Wu, Tan Lee, "Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5343. Accessed: Jul. 14, 2020.
@article{5343-20,
url = {http://sigport.org/5343},
author = {Yuzhong Wu; Tan Lee },
publisher = {IEEE SigPort},
title = {Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification},
year = {2020} }
TY - EJOUR
T1 - Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification
AU - Yuzhong Wu; Tan Lee
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5343
ER -
Yuzhong Wu, Tan Lee. (2020). Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification. IEEE SigPort. http://sigport.org/5343
Yuzhong Wu, Tan Lee, 2020. Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification. Available at: http://sigport.org/5343.
Yuzhong Wu, Tan Lee. (2020). "Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification." Web.
1. Yuzhong Wu, Tan Lee. Time-Frequency Feature Decomposition Based on Sound Duration for Acoustic Scene Classification [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5343

β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR


In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method.

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Authors:
Sandrine ANTHOINE, Caroline CHAUX
Submitted On:
15 May 2020 - 4:42am
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ICASSP2020_CHERNI.pdf

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[1] Sandrine ANTHOINE, Caroline CHAUX, "β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5342. Accessed: Jul. 14, 2020.
@article{5342-20,
url = {http://sigport.org/5342},
author = {Sandrine ANTHOINE; Caroline CHAUX },
publisher = {IEEE SigPort},
title = {β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR},
year = {2020} }
TY - EJOUR
T1 - β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR
AU - Sandrine ANTHOINE; Caroline CHAUX
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5342
ER -
Sandrine ANTHOINE, Caroline CHAUX. (2020). β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR. IEEE SigPort. http://sigport.org/5342
Sandrine ANTHOINE, Caroline CHAUX, 2020. β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR. Available at: http://sigport.org/5342.
Sandrine ANTHOINE, Caroline CHAUX. (2020). "β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR." Web.
1. Sandrine ANTHOINE, Caroline CHAUX. β-NMF and sparsity promoting regularizations for complex mixture unmixing: Application to 2D HSQC NMR [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5342

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