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ICASSP 2018

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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.

FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR


This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

Paper Details

Authors:
Di Sheng, Gyorgy Fazekas
Submitted On:
12 April 2018 - 1:40pm
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[1] Di Sheng, Gyorgy Fazekas, "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2453. Accessed: May. 19, 2019.
@article{2453-18,
url = {http://sigport.org/2453},
author = {Di Sheng; Gyorgy Fazekas },
publisher = {IEEE SigPort},
title = {FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR},
year = {2018} }
TY - EJOUR
T1 - FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR
AU - Di Sheng; Gyorgy Fazekas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2453
ER -
Di Sheng, Gyorgy Fazekas. (2018). FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. IEEE SigPort. http://sigport.org/2453
Di Sheng, Gyorgy Fazekas, 2018. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. Available at: http://sigport.org/2453.
Di Sheng, Gyorgy Fazekas. (2018). "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR." Web.
1. Di Sheng, Gyorgy Fazekas. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2453

FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR


This paper proposes a method of controlling the dynamic range compressor using sound examples. Our earlier work showed the effectiveness of random forest regression to map acoustic features to effect control parameters. We extend this work to address the challenging task of extracting relevant features when audio events overlap. We assess differ- ent audio decomposition approaches such as onset event detection, NMF, and transient/stationary audio separation using ISTA and compare feature extraction strategies for each case.

Paper Details

Authors:
Di Sheng, Gyorgy Fazekas
Submitted On:
12 April 2018 - 1:40pm
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Type:
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poster_icassp.pdf

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[1] Di Sheng, Gyorgy Fazekas, "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2452. Accessed: May. 19, 2019.
@article{2452-18,
url = {http://sigport.org/2452},
author = {Di Sheng; Gyorgy Fazekas },
publisher = {IEEE SigPort},
title = {FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR},
year = {2018} }
TY - EJOUR
T1 - FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR
AU - Di Sheng; Gyorgy Fazekas
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2452
ER -
Di Sheng, Gyorgy Fazekas. (2018). FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. IEEE SigPort. http://sigport.org/2452
Di Sheng, Gyorgy Fazekas, 2018. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR. Available at: http://sigport.org/2452.
Di Sheng, Gyorgy Fazekas. (2018). "FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR." Web.
1. Di Sheng, Gyorgy Fazekas. FEATURE DESIGN USING AUDIO DECOMPOSITION FOR INTELLIGENT CONTROL OF THE DYNAMIC RANGE COMPRESSOR [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2452

Binaural Speech Source Localization using template matching of interaural time difference patterns

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Authors:
Karthik Girija Ramesan, Prasanta Kumar Ghosh
Submitted On:
12 April 2018 - 1:33pm
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GRKARTHIK_ICASSP_Poster_final.pdf

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[1] Karthik Girija Ramesan, Prasanta Kumar Ghosh, "Binaural Speech Source Localization using template matching of interaural time difference patterns", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2451. Accessed: May. 19, 2019.
@article{2451-18,
url = {http://sigport.org/2451},
author = {Karthik Girija Ramesan; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {Binaural Speech Source Localization using template matching of interaural time difference patterns},
year = {2018} }
TY - EJOUR
T1 - Binaural Speech Source Localization using template matching of interaural time difference patterns
AU - Karthik Girija Ramesan; Prasanta Kumar Ghosh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2451
ER -
Karthik Girija Ramesan, Prasanta Kumar Ghosh. (2018). Binaural Speech Source Localization using template matching of interaural time difference patterns. IEEE SigPort. http://sigport.org/2451
Karthik Girija Ramesan, Prasanta Kumar Ghosh, 2018. Binaural Speech Source Localization using template matching of interaural time difference patterns. Available at: http://sigport.org/2451.
Karthik Girija Ramesan, Prasanta Kumar Ghosh. (2018). "Binaural Speech Source Localization using template matching of interaural time difference patterns." Web.
1. Karthik Girija Ramesan, Prasanta Kumar Ghosh. Binaural Speech Source Localization using template matching of interaural time difference patterns [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2451

ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW


In this paper, we propose an adaptive STFT (ASTFT) with adaptive chirp-modulated Gaussian window. The window is obtained from rotating Gaussian function in time-frequency plane by fractional Fourier transform (FRFT). It is completely adaptive where the two parameters, FRFT rotation angle and Gaussian variance, are signal-dependent. The angle dependents on the chirp rate of the signal. The variance is determined by the chirp rate and its first derivative.

Paper Details

Authors:
Soo-Chang Pei, Shih-Gu Huang
Submitted On:
12 April 2018 - 1:20pm
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ICASSP20180413_Poster_Huang.pdf

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[1] Soo-Chang Pei, Shih-Gu Huang, "ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2450. Accessed: May. 19, 2019.
@article{2450-18,
url = {http://sigport.org/2450},
author = {Soo-Chang Pei; Shih-Gu Huang },
publisher = {IEEE SigPort},
title = {ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW},
year = {2018} }
TY - EJOUR
T1 - ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW
AU - Soo-Chang Pei; Shih-Gu Huang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2450
ER -
Soo-Chang Pei, Shih-Gu Huang. (2018). ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW. IEEE SigPort. http://sigport.org/2450
Soo-Chang Pei, Shih-Gu Huang, 2018. ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW. Available at: http://sigport.org/2450.
Soo-Chang Pei, Shih-Gu Huang. (2018). "ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW." Web.
1. Soo-Chang Pei, Shih-Gu Huang. ADAPTIVE STFT WITH CHIRP-MODULATED GAUSSIAN WINDOW [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2450

IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS


In this paper, we propose a scheme to simplify the selection of kernel adaptive filters in a multikernel structure.
By multiplying the output of each kernel filter by an adaptive biasing factor between zero and one, the degrading effects of poorly adjusted kernel filters can be minimized, increasing the robustness of the multikernel scheme. This approach is able to deal with the lack of the necessary statistical information for an optimal adjustment of the filter and its structure.

Paper Details

Authors:
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz
Submitted On:
12 April 2018 - 1:24pm
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Poster_ICASSP2018_Paper#4215.pdf

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[1] Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, "IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2449. Accessed: May. 19, 2019.
@article{2449-18,
url = {http://sigport.org/2449},
author = {Magno T. M. Silva; Renato Candido; Jerónimo Arenas-García; Luis A. Azpicueta-Ruiz },
publisher = {IEEE SigPort},
title = {IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS},
year = {2018} }
TY - EJOUR
T1 - IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS
AU - Magno T. M. Silva; Renato Candido; Jerónimo Arenas-García; Luis A. Azpicueta-Ruiz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2449
ER -
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. (2018). IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS. IEEE SigPort. http://sigport.org/2449
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz, 2018. IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS. Available at: http://sigport.org/2449.
Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. (2018). "IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS." Web.
1. Magno T. M. Silva, Renato Candido, Jerónimo Arenas-García, Luis A. Azpicueta-Ruiz. IMPROVING MULTIKERNEL ADAPTIVE FILTERING WITH SELECTIVE BIAS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2449

OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY

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Authors:
Yonina C. Eldar
Submitted On:
12 April 2018 - 1:13pm
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SparseArrays_ICASSP2018.pdf

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[1] Yonina C. Eldar, "OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2448. Accessed: May. 19, 2019.
@article{2448-18,
url = {http://sigport.org/2448},
author = {Yonina C. Eldar },
publisher = {IEEE SigPort},
title = {OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY},
year = {2018} }
TY - EJOUR
T1 - OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY
AU - Yonina C. Eldar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2448
ER -
Yonina C. Eldar. (2018). OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY. IEEE SigPort. http://sigport.org/2448
Yonina C. Eldar, 2018. OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY. Available at: http://sigport.org/2448.
Yonina C. Eldar. (2018). "OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY." Web.
1. Yonina C. Eldar. OPTIMIZED SPARSE ARRAY DESIGN BASED ON THE SUM CO-ARRAY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2448

Making Likelihood Ratios Digestible for Cross-Application Performance Assessment


Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error trade-off (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels.

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Authors:
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch
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12 April 2018 - 1:11pm
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[1] Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2447. Accessed: May. 19, 2019.
@article{2447-18,
url = {http://sigport.org/2447},
author = {Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch },
publisher = {IEEE SigPort},
title = {Making Likelihood Ratios Digestible for Cross-Application Performance Assessment},
year = {2018} }
TY - EJOUR
T1 - Making Likelihood Ratios Digestible for Cross-Application Performance Assessment
AU - Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2447
ER -
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. IEEE SigPort. http://sigport.org/2447
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, 2018. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. Available at: http://sigport.org/2447.
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment." Web.
1. Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2447

Graph learning based on total variation minimization

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Authors:
Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz
Submitted On:
12 April 2018 - 1:10pm
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[1] Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz, "Graph learning based on total variation minimization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2446. Accessed: May. 19, 2019.
@article{2446-18,
url = {http://sigport.org/2446},
author = {Peter Berger; Manfred Buchacher; Gabor Hannak; Gerald Matz },
publisher = {IEEE SigPort},
title = {Graph learning based on total variation minimization},
year = {2018} }
TY - EJOUR
T1 - Graph learning based on total variation minimization
AU - Peter Berger; Manfred Buchacher; Gabor Hannak; Gerald Matz
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2446
ER -
Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz. (2018). Graph learning based on total variation minimization. IEEE SigPort. http://sigport.org/2446
Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz, 2018. Graph learning based on total variation minimization. Available at: http://sigport.org/2446.
Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz. (2018). "Graph learning based on total variation minimization." Web.
1. Peter Berger, Manfred Buchacher, Gabor Hannak, Gerald Matz. Graph learning based on total variation minimization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2446

Convolutional group-sparse coding and source localization


In this paper, we present a new interpretation of non-negatively constrained convolutional coding problems as blind deconvolution problems with spatially variant point spread function. In this light, we propose an optimization framework that generalizes our previous work on non-negative group sparsity for convolutional models. We then link these concepts to source localization problems that arise in scientific imaging, and provide a visual example on an image derived from data captured by the Hubble telescope.

Paper Details

Authors:
Pol del Aguila Pla, Joakim Jaldén
Submitted On:
12 April 2018 - 12:59pm
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[1] Pol del Aguila Pla, Joakim Jaldén, "Convolutional group-sparse coding and source localization", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2444. Accessed: May. 19, 2019.
@article{2444-18,
url = {http://sigport.org/2444},
author = {Pol del Aguila Pla; Joakim Jaldén },
publisher = {IEEE SigPort},
title = {Convolutional group-sparse coding and source localization},
year = {2018} }
TY - EJOUR
T1 - Convolutional group-sparse coding and source localization
AU - Pol del Aguila Pla; Joakim Jaldén
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2444
ER -
Pol del Aguila Pla, Joakim Jaldén. (2018). Convolutional group-sparse coding and source localization. IEEE SigPort. http://sigport.org/2444
Pol del Aguila Pla, Joakim Jaldén, 2018. Convolutional group-sparse coding and source localization. Available at: http://sigport.org/2444.
Pol del Aguila Pla, Joakim Jaldén. (2018). "Convolutional group-sparse coding and source localization." Web.
1. Pol del Aguila Pla, Joakim Jaldén. Convolutional group-sparse coding and source localization [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2444

AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING


Magnetic resonance (MR) plays an important role in medical imaging. It can be flexibly tuned towards different applications for deriving a meaningful diagnosis. However, its long acquisition times and flexible parametrization make it on the other hand prone to artifacts which obscure the underlying image content or can be misinterpreted as anatomy. Patient-induced motion artifacts are still one of the major extrinsic factors which degrade image quality.

Paper Details

Authors:
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang
Submitted On:
12 April 2018 - 12:45pm
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poster_icassp2018.pdf

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[1] Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2443. Accessed: May. 19, 2019.
@article{2443-18,
url = {http://sigport.org/2443},
author = {Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang },
publisher = {IEEE SigPort},
title = {AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING
AU - Thomas Küstner; Marvin Jandt; Annika Liebgott; Lukas Mauch; Petros Martirosian; Fabian Bamberg; Konstantin Nikolaou; Sergios Gatidis; Fritz Schick; Bin Yang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2443
ER -
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. IEEE SigPort. http://sigport.org/2443
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang, 2018. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING. Available at: http://sigport.org/2443.
Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. (2018). "AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING." Web.
1. Thomas Küstner, Marvin Jandt, Annika Liebgott, Lukas Mauch, Petros Martirosian, Fabian Bamberg, Konstantin Nikolaou, Sergios Gatidis, Fritz Schick, Bin Yang. AUTOMATIC MOTION ARTIFACT DETECTION FOR WHOLE-BODY MAGNETIC RESONANCE IMAGING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2443

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