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ICASSP 2017

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2017 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2017

OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS

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22 May 2017 - 11:56pm
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Naveen ICASSP17 PhD Forum Paper

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[1] , " OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1796. Accessed: Jul. 27, 2017.
@article{1796-17,
url = {http://sigport.org/1796},
author = { },
publisher = {IEEE SigPort},
title = { OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS},
year = {2017} }
TY - EJOUR
T1 - OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1796
ER -
. (2017). OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS. IEEE SigPort. http://sigport.org/1796
, 2017. OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS. Available at: http://sigport.org/1796.
. (2017). " OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS." Web.
1. . OPTIMAL TRANSCEIVER DESIGN IN MULTI-USER MULTIPLE-INPUT MULTIPLE-OUTPUT WIRELESS NETWORKS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1796

Decorrelation for Audio Object Coding


Object-based representations of audio content are increasingly
used in entertainment systems to deliver immersive and
personalized experiences. Efficient storage and transmission
of such content can be achieved by joint object coding algorithms
that convey a reduced number of downmix signals
together with parametric side information that enables object
reconstruction in the decoder. This paper presents an
approach to improve the performance of joint object coding
by adding one or more decorrelators to the decoding process.

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Authors:
Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen
Submitted On:
19 May 2017 - 7:40am
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ICASSP2017_FINAL.pdf

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[1] Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen, "Decorrelation for Audio Object Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1795. Accessed: Jul. 27, 2017.
@article{1795-17,
url = {http://sigport.org/1795},
author = {Lars Villemoes; Toni Hirvonen; and Heiko Purnhagen },
publisher = {IEEE SigPort},
title = {Decorrelation for Audio Object Coding},
year = {2017} }
TY - EJOUR
T1 - Decorrelation for Audio Object Coding
AU - Lars Villemoes; Toni Hirvonen; and Heiko Purnhagen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1795
ER -
Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen. (2017). Decorrelation for Audio Object Coding. IEEE SigPort. http://sigport.org/1795
Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen, 2017. Decorrelation for Audio Object Coding. Available at: http://sigport.org/1795.
Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen. (2017). "Decorrelation for Audio Object Coding." Web.
1. Lars Villemoes, Toni Hirvonen, and Heiko Purnhagen. Decorrelation for Audio Object Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1795

FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION


Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images. Then a neural network classifier is been trained to decide whether a superpixel is road region or not. Finally, the classified results are further refined by conditional random field.

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Authors:
Guorong Cai,Zhun Zhong,Songzhi Su
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8 May 2017 - 5:17am
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poster_hwl.pdf

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[1] Guorong Cai,Zhun Zhong,Songzhi Su, "FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1794. Accessed: Jul. 27, 2017.
@article{1794-17,
url = {http://sigport.org/1794},
author = {Guorong Cai;Zhun Zhong;Songzhi Su },
publisher = {IEEE SigPort},
title = {FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION},
year = {2017} }
TY - EJOUR
T1 - FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION
AU - Guorong Cai;Zhun Zhong;Songzhi Su
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1794
ER -
Guorong Cai,Zhun Zhong,Songzhi Su. (2017). FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION. IEEE SigPort. http://sigport.org/1794
Guorong Cai,Zhun Zhong,Songzhi Su, 2017. FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION. Available at: http://sigport.org/1794.
Guorong Cai,Zhun Zhong,Songzhi Su. (2017). "FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION." Web.
1. Guorong Cai,Zhun Zhong,Songzhi Su. FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1794

FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION


Road detection is a key component of Advanced Driving Assistance Systems, which provides valid space and candidate regions of objects for vehicles. Mainstream road detection methods have focused on extracting discriminative features. In this paper, we propose a robust feature fusion framework, called “Feature++”, which is combined with superpixel feature and 3D feature extracted from stereo images. Then a neural network classifier is been trained to decide whether a superpixel is road region or not. Finally, the classified results are further refined by conditional random field.

Paper Details

Authors:
Guorong Cai,Zhun Zhong,Songzhi Su
Submitted On:
8 May 2017 - 5:17am
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poster_hwl.pdf

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[1] Guorong Cai,Zhun Zhong,Songzhi Su, "FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1793. Accessed: Jul. 27, 2017.
@article{1793-17,
url = {http://sigport.org/1793},
author = {Guorong Cai;Zhun Zhong;Songzhi Su },
publisher = {IEEE SigPort},
title = {FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION},
year = {2017} }
TY - EJOUR
T1 - FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION
AU - Guorong Cai;Zhun Zhong;Songzhi Su
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1793
ER -
Guorong Cai,Zhun Zhong,Songzhi Su. (2017). FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION. IEEE SigPort. http://sigport.org/1793
Guorong Cai,Zhun Zhong,Songzhi Su, 2017. FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION. Available at: http://sigport.org/1793.
Guorong Cai,Zhun Zhong,Songzhi Su. (2017). "FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION." Web.
1. Guorong Cai,Zhun Zhong,Songzhi Su. FEATURE++: CROSS DIMENSION FEATURE FUSION FOR ROAD DETECTION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1793

Time of Arrival Disambiguation Using the Linear Radon Transform


Echo labeling, the challenging task of assigning acoustic reflections to image sources, is equivalent to the highly-important disambiguation task in room geometry inference. A method using the Radon transform, an image processing tool, is proposed to address this challenge. The method relies on acoustic wavefront detection in room impulse response stacks, obtained with a uniform linear array of loudspeakers and one microphone. We show in our experiments that the proposed method can both label and detect echoes.

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Authors:
Youssef El Baba, Andreas Walther, Emanuël Habets
Submitted On:
5 April 2017 - 11:44am
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LRT-SL talk.pdf

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[1] Youssef El Baba, Andreas Walther, Emanuël Habets, "Time of Arrival Disambiguation Using the Linear Radon Transform", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1790. Accessed: Jul. 27, 2017.
@article{1790-17,
url = {http://sigport.org/1790},
author = {Youssef El Baba; Andreas Walther; Emanuël Habets },
publisher = {IEEE SigPort},
title = {Time of Arrival Disambiguation Using the Linear Radon Transform},
year = {2017} }
TY - EJOUR
T1 - Time of Arrival Disambiguation Using the Linear Radon Transform
AU - Youssef El Baba; Andreas Walther; Emanuël Habets
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1790
ER -
Youssef El Baba, Andreas Walther, Emanuël Habets. (2017). Time of Arrival Disambiguation Using the Linear Radon Transform. IEEE SigPort. http://sigport.org/1790
Youssef El Baba, Andreas Walther, Emanuël Habets, 2017. Time of Arrival Disambiguation Using the Linear Radon Transform. Available at: http://sigport.org/1790.
Youssef El Baba, Andreas Walther, Emanuël Habets. (2017). "Time of Arrival Disambiguation Using the Linear Radon Transform." Web.
1. Youssef El Baba, Andreas Walther, Emanuël Habets. Time of Arrival Disambiguation Using the Linear Radon Transform [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1790

Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture


Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel.

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Authors:
Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic
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29 March 2017 - 1:00pm
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Time_domain_ch_est_wideband_hybrid_mmWave_v2.pdf

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[1] Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic, "Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1785. Accessed: Jul. 27, 2017.
@article{1785-17,
url = {http://sigport.org/1785},
author = {Kiran Venugopal; Ahmed Alkhateeb; Robert W. Heath; Jr. and Nuria Gonzalez Prelcic },
publisher = {IEEE SigPort},
title = {Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture},
year = {2017} }
TY - EJOUR
T1 - Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture
AU - Kiran Venugopal; Ahmed Alkhateeb; Robert W. Heath; Jr. and Nuria Gonzalez Prelcic
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1785
ER -
Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic. (2017). Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture. IEEE SigPort. http://sigport.org/1785
Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic, 2017. Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture. Available at: http://sigport.org/1785.
Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic. (2017). "Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture." Web.
1. Kiran Venugopal, Ahmed Alkhateeb, Robert W. Heath, Jr. and Nuria Gonzalez Prelcic. Time-Domain Channel Estimation for Wideband Millimeter Wave Systems With Hybrid Architecture [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1785

Patch-based Multiple View Image Denoising with Occlusion Handling

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Authors:
Shiwei Zhou, Yu Hen Hu, Hongrui Jiang
Submitted On:
23 March 2017 - 2:49pm
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ICASSP_poster.pdf

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[1] Shiwei Zhou, Yu Hen Hu, Hongrui Jiang, "Patch-based Multiple View Image Denoising with Occlusion Handling", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1782. Accessed: Jul. 27, 2017.
@article{1782-17,
url = {http://sigport.org/1782},
author = {Shiwei Zhou; Yu Hen Hu; Hongrui Jiang },
publisher = {IEEE SigPort},
title = {Patch-based Multiple View Image Denoising with Occlusion Handling},
year = {2017} }
TY - EJOUR
T1 - Patch-based Multiple View Image Denoising with Occlusion Handling
AU - Shiwei Zhou; Yu Hen Hu; Hongrui Jiang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1782
ER -
Shiwei Zhou, Yu Hen Hu, Hongrui Jiang. (2017). Patch-based Multiple View Image Denoising with Occlusion Handling. IEEE SigPort. http://sigport.org/1782
Shiwei Zhou, Yu Hen Hu, Hongrui Jiang, 2017. Patch-based Multiple View Image Denoising with Occlusion Handling. Available at: http://sigport.org/1782.
Shiwei Zhou, Yu Hen Hu, Hongrui Jiang. (2017). "Patch-based Multiple View Image Denoising with Occlusion Handling." Web.
1. Shiwei Zhou, Yu Hen Hu, Hongrui Jiang. Patch-based Multiple View Image Denoising with Occlusion Handling [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1782

AFFECT RECOGNITION FROM LIP ARTICULATIONS

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23 March 2017 - 1:40pm
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Poster_ICASSP17_Rizwan.pdf

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[1] , "AFFECT RECOGNITION FROM LIP ARTICULATIONS", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1781. Accessed: Jul. 27, 2017.
@article{1781-17,
url = {http://sigport.org/1781},
author = { },
publisher = {IEEE SigPort},
title = {AFFECT RECOGNITION FROM LIP ARTICULATIONS},
year = {2017} }
TY - EJOUR
T1 - AFFECT RECOGNITION FROM LIP ARTICULATIONS
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1781
ER -
. (2017). AFFECT RECOGNITION FROM LIP ARTICULATIONS. IEEE SigPort. http://sigport.org/1781
, 2017. AFFECT RECOGNITION FROM LIP ARTICULATIONS. Available at: http://sigport.org/1781.
. (2017). "AFFECT RECOGNITION FROM LIP ARTICULATIONS." Web.
1. . AFFECT RECOGNITION FROM LIP ARTICULATIONS [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1781

PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING


Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning.

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Authors:
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers
Submitted On:
22 March 2017 - 8:04am
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ICASSP2017-POSTER (1).pdf

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[1] Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1780. Accessed: Jul. 27, 2017.
@article{1780-17,
url = {http://sigport.org/1780},
author = {Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers },
publisher = {IEEE SigPort},
title = {PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING},
year = {2017} }
TY - EJOUR
T1 - PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING
AU - Zeyu Fu; Pengming Feng; Syed Mohsen Naqvi; and Jonathon Chambers
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1780
ER -
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. IEEE SigPort. http://sigport.org/1780
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers, 2017. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING. Available at: http://sigport.org/1780.
Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. (2017). "PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING." Web.
1. Zeyu Fu, Pengming Feng, Syed Mohsen Naqvi, and Jonathon Chambers. PARTICLE PHD FILTER BASED MULTI-TARGET TRACKING USING DISCRIMINATIVE GROUP-STRUCTURED DICTIONARY LEARNING [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1780

RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION

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Authors:
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf
Submitted On:
20 March 2017 - 12:25pm
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poster.pdf

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[1] Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf, "RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1778. Accessed: Jul. 27, 2017.
@article{1778-17,
url = {http://sigport.org/1778},
author = {Elmar Messner; Martin Hagmüller; Paul Swatek; Freyja-Maria Smolle-Jüttner; Franz Pernkopf },
publisher = {IEEE SigPort},
title = {RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION},
year = {2017} }
TY - EJOUR
T1 - RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION
AU - Elmar Messner; Martin Hagmüller; Paul Swatek; Freyja-Maria Smolle-Jüttner; Franz Pernkopf
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1778
ER -
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. (2017). RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION. IEEE SigPort. http://sigport.org/1778
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf, 2017. RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION. Available at: http://sigport.org/1778.
Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. (2017). "RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION." Web.
1. Elmar Messner, Martin Hagmüller, Paul Swatek, Freyja-Maria Smolle-Jüttner, Franz Pernkopf. RESPIRATORY AIRFLOW ESTIMATION FROM LUNG SOUNDS BASED ON REGRESSION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1778

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