Sorry, you need to enable JavaScript to visit this website.

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.

Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO

Paper Details

Authors:
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson
Submitted On:
20 May 2020 - 1:16am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO

(26)

Subscribe

[1] Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson, "Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5402. Accessed: Aug. 12, 2020.
@article{5402-20,
url = {http://sigport.org/5402},
author = {Jinu Jayachandran; Kamal Biswas; Saif Khan Mohammed; Erik G. Larsson },
publisher = {IEEE SigPort},
title = {Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO},
year = {2020} }
TY - EJOUR
T1 - Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO
AU - Jinu Jayachandran; Kamal Biswas; Saif Khan Mohammed; Erik G. Larsson
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5402
ER -
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. (2020). Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO. IEEE SigPort. http://sigport.org/5402
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson, 2020. Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO. Available at: http://sigport.org/5402.
Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. (2020). "Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO." Web.
1. Jinu Jayachandran, Kamal Biswas, Saif Khan Mohammed, Erik G. Larsson. Efficient Techniques for In-band System Information Broadcast in Multi-cell Massive MIMO [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5402

FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR


Bridge weigh-in-motion (BWIM) is a technique for detecting heavy vehicles that may cause serious damage to real bridges. BWIM is realized by analyzing the strain signals observed at places on the bridge in terms of bridge-component responses to the axle loads. In current practice, a BWIM system requires multiple strain sensors to collect vehicle properties including speed and axle positions for accurate load estimation, which may limit the system’s life-span.

Paper Details

Authors:
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi
Submitted On:
27 May 2020 - 11:14am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp20s.pdf

(25)

Keywords

Additional Categories

Subscribe

[1] Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5401. Accessed: Aug. 12, 2020.
@article{5401-20,
url = {http://sigport.org/5401},
author = {Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi },
publisher = {IEEE SigPort},
title = {FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR},
year = {2020} }
TY - EJOUR
T1 - FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR
AU - Takaya Kawakatsu; Kenro Aihara; Atsuhiro Takasu; Jun Adachi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5401
ER -
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. IEEE SigPort. http://sigport.org/5401
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi, 2020. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR. Available at: http://sigport.org/5401.
Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. (2020). "FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR." Web.
1. Takaya Kawakatsu, Kenro Aihara, Atsuhiro Takasu, Jun Adachi. FULLY-NEURAL APPROACH TO HEAVY VEHICLE DETECTION ON BRIDGES USING A SINGLE STRAIN SENSOR [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5401

Conditional Density Driven Grid Design in Point-Mass Filter

Paper Details

Authors:
Submitted On:
19 May 2020 - 8:27am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

presentation.pdf

(23)

Subscribe

[1] , "Conditional Density Driven Grid Design in Point-Mass Filter", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5400. Accessed: Aug. 12, 2020.
@article{5400-20,
url = {http://sigport.org/5400},
author = { },
publisher = {IEEE SigPort},
title = {Conditional Density Driven Grid Design in Point-Mass Filter},
year = {2020} }
TY - EJOUR
T1 - Conditional Density Driven Grid Design in Point-Mass Filter
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5400
ER -
. (2020). Conditional Density Driven Grid Design in Point-Mass Filter. IEEE SigPort. http://sigport.org/5400
, 2020. Conditional Density Driven Grid Design in Point-Mass Filter. Available at: http://sigport.org/5400.
. (2020). "Conditional Density Driven Grid Design in Point-Mass Filter." Web.
1. . Conditional Density Driven Grid Design in Point-Mass Filter [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5400

A REAL-TIME DEEP NETWORK FOR CROWD COUNTING


Automatic analysis of highly crowded people has attracted extensive attention from computer vision research. Previous approaches for crowd counting have already achieved promising performance across various benchmarks. However, to deal with the real situation, we hope the model run as fast as possible while keeping accuracy. In this paper, we propose a compact convolutional neural network for crowd counting which learns a more efficient model with a small number of parameters.

Paper Details

Authors:
Submitted On:
19 May 2020 - 6:14am
Short Link:
Type:
Event:

Document Files

ICASSP_2020_XShi_PPT.pdf

(22)

Subscribe

[1] , "A REAL-TIME DEEP NETWORK FOR CROWD COUNTING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5399. Accessed: Aug. 12, 2020.
@article{5399-20,
url = {http://sigport.org/5399},
author = { },
publisher = {IEEE SigPort},
title = {A REAL-TIME DEEP NETWORK FOR CROWD COUNTING},
year = {2020} }
TY - EJOUR
T1 - A REAL-TIME DEEP NETWORK FOR CROWD COUNTING
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5399
ER -
. (2020). A REAL-TIME DEEP NETWORK FOR CROWD COUNTING. IEEE SigPort. http://sigport.org/5399
, 2020. A REAL-TIME DEEP NETWORK FOR CROWD COUNTING. Available at: http://sigport.org/5399.
. (2020). "A REAL-TIME DEEP NETWORK FOR CROWD COUNTING." Web.
1. . A REAL-TIME DEEP NETWORK FOR CROWD COUNTING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5399

View-angle Invariant Object Monitoring Without Image Registration


Object monitoring can be performed by change detection algorithms. However, for the image pair with a large perspective difference, the change detection performance is usually impacted by inaccurate image registration. To address the above difficulties, a novel object-specific change detection approach is proposed for object monitoring in this paper. In contrast to traditional approaches, the proposed approach is robust to view angle variation and does not require explicit image registration. Experiments demonstrate the effectiveness and advantages of the proposed approach.

Paper Details

Authors:
Xin Zhang; Chunlei Huo; Chunhong Pan
Submitted On:
19 May 2020 - 4:58am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

View-angle Invariant Object Monitoring Without Image Registration .pdf

(31)

Subscribe

[1] Xin Zhang; Chunlei Huo; Chunhong Pan, "View-angle Invariant Object Monitoring Without Image Registration ", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5398. Accessed: Aug. 12, 2020.
@article{5398-20,
url = {http://sigport.org/5398},
author = {Xin Zhang; Chunlei Huo; Chunhong Pan },
publisher = {IEEE SigPort},
title = {View-angle Invariant Object Monitoring Without Image Registration },
year = {2020} }
TY - EJOUR
T1 - View-angle Invariant Object Monitoring Without Image Registration
AU - Xin Zhang; Chunlei Huo; Chunhong Pan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5398
ER -
Xin Zhang; Chunlei Huo; Chunhong Pan. (2020). View-angle Invariant Object Monitoring Without Image Registration . IEEE SigPort. http://sigport.org/5398
Xin Zhang; Chunlei Huo; Chunhong Pan, 2020. View-angle Invariant Object Monitoring Without Image Registration . Available at: http://sigport.org/5398.
Xin Zhang; Chunlei Huo; Chunhong Pan. (2020). "View-angle Invariant Object Monitoring Without Image Registration ." Web.
1. Xin Zhang; Chunlei Huo; Chunhong Pan. View-angle Invariant Object Monitoring Without Image Registration [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5398

IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES


The recent success of the Transformer based sequence-to-sequence framework for various Natural Language Processing tasks has motivated its application to Automatic Speech Recognition. In this work, we explore the application of Transformers on low resource Indian languages in a multilingual framework. We explore various methods to incorporate language information into a multilingual Transformer, i.e.,(i) at the decoder, (ii) at the encoder. These methods include using language identity tokens or providing language information to the acoustic vectors.

Paper Details

Authors:
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh
Submitted On:
19 May 2020 - 3:23am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

shetty.pdf

(24)

Subscribe

[1] Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh, "IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5397. Accessed: Aug. 12, 2020.
@article{5397-20,
url = {http://sigport.org/5397},
author = {Vishwas M. Shetty; Metilda Sagaya Mary N J; S. Umesh },
publisher = {IEEE SigPort},
title = {IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES},
year = {2020} }
TY - EJOUR
T1 - IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES
AU - Vishwas M. Shetty; Metilda Sagaya Mary N J; S. Umesh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5397
ER -
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. (2020). IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES. IEEE SigPort. http://sigport.org/5397
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh, 2020. IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES. Available at: http://sigport.org/5397.
Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. (2020). "IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES." Web.
1. Vishwas M. Shetty, Metilda Sagaya Mary N J, S. Umesh. IMPROVING THE PERFORMANCE OF TRANSFORMER BASED LOW RESOURCE SPEECH RECOGNITION FOR INDIAN LANGUAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5397

WITCHcraft: Efficient PGD Attacks with Random Step Size

Paper Details

Authors:
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi
Submitted On:
18 May 2020 - 6:05pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

WITCHcraft ICASSP 2020.pdf

(26)

Subscribe

[1] Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, "WITCHcraft: Efficient PGD Attacks with Random Step Size", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5396. Accessed: Aug. 12, 2020.
@article{5396-20,
url = {http://sigport.org/5396},
author = {Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi },
publisher = {IEEE SigPort},
title = {WITCHcraft: Efficient PGD Attacks with Random Step Size},
year = {2020} }
TY - EJOUR
T1 - WITCHcraft: Efficient PGD Attacks with Random Step Size
AU - Ping-Yeh Chiang; Jonas Geiping; Micah Goldblum; Tom Goldstein; Renkun Ni; Steven Reich; Ali Shafahi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5396
ER -
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). WITCHcraft: Efficient PGD Attacks with Random Step Size. IEEE SigPort. http://sigport.org/5396
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi, 2020. WITCHcraft: Efficient PGD Attacks with Random Step Size. Available at: http://sigport.org/5396.
Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. (2020). "WITCHcraft: Efficient PGD Attacks with Random Step Size." Web.
1. Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Tom Goldstein, Renkun Ni, Steven Reich, Ali Shafahi. WITCHcraft: Efficient PGD Attacks with Random Step Size [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5396

DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES


In this work, we present speech recognition systems for four Ethiopian languages: Amharic, Tigrigna, Oromo and Wolaytta. We have used comparable training corpora of about 20 to 29 hours speech and evaluation speech of about 1 hour for each of the languages. For Amharic and Tigrigna, lexical and language models of different vocabulary size have been developed. For Oromo and Wolaytta, the training lexicons have been used for decoding.

Paper Details

Authors:
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz
Submitted On:
20 May 2020 - 9:24am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:

Document Files

MarthaSolomonTanja.pdf

(34)

Subscribe

[1] Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz, "DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5395. Accessed: Aug. 12, 2020.
@article{5395-20,
url = {http://sigport.org/5395},
author = {Solomon Teferra Abate;Martha Yifiru Tachbelie and Tanja Schultz },
publisher = {IEEE SigPort},
title = {DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES},
year = {2020} }
TY - EJOUR
T1 - DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES
AU - Solomon Teferra Abate;Martha Yifiru Tachbelie and Tanja Schultz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5395
ER -
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. (2020). DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES. IEEE SigPort. http://sigport.org/5395
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz, 2020. DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES. Available at: http://sigport.org/5395.
Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. (2020). "DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES." Web.
1. Solomon Teferra Abate,Martha Yifiru Tachbelie and Tanja Schultz. DEEP NEURAL NETWORKS BASED AUTOMATIC SPEECH RECOGNITION FOR FOUR ETHIOPIAN LANGUAGES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5395

Effects of Spectral Tilt on Listeners' Preferences And Intelligibility

Paper Details

Authors:
Olympia Simantiraki, Martin Cooke, Yannis Pantazis
Submitted On:
18 May 2020 - 12:27pm
Short Link:
Type:
Event:

Document Files

ICASSP2020_simantiraki.pdf

(28)

Subscribe

[1] Olympia Simantiraki, Martin Cooke, Yannis Pantazis, "Effects of Spectral Tilt on Listeners' Preferences And Intelligibility", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5394. Accessed: Aug. 12, 2020.
@article{5394-20,
url = {http://sigport.org/5394},
author = {Olympia Simantiraki; Martin Cooke; Yannis Pantazis },
publisher = {IEEE SigPort},
title = {Effects of Spectral Tilt on Listeners' Preferences And Intelligibility},
year = {2020} }
TY - EJOUR
T1 - Effects of Spectral Tilt on Listeners' Preferences And Intelligibility
AU - Olympia Simantiraki; Martin Cooke; Yannis Pantazis
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5394
ER -
Olympia Simantiraki, Martin Cooke, Yannis Pantazis. (2020). Effects of Spectral Tilt on Listeners' Preferences And Intelligibility. IEEE SigPort. http://sigport.org/5394
Olympia Simantiraki, Martin Cooke, Yannis Pantazis, 2020. Effects of Spectral Tilt on Listeners' Preferences And Intelligibility. Available at: http://sigport.org/5394.
Olympia Simantiraki, Martin Cooke, Yannis Pantazis. (2020). "Effects of Spectral Tilt on Listeners' Preferences And Intelligibility." Web.
1. Olympia Simantiraki, Martin Cooke, Yannis Pantazis. Effects of Spectral Tilt on Listeners' Preferences And Intelligibility [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5394

BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING


Single image deraining has been widely studied in recent years. Motivated by residual learning, most deep learning based deraining approaches devote research attention to extracting rain streaks, usually yielding visual artifacts in final deraining images. To address this issue, we in this paper propose bilateral recurrent network (BRN) to simultaneously exploit rain streak layer and background image layer. Generally, we employ dual residual networks (ResNet) that are recursively unfolded to sequentially extract rain streaks and predict clean background image.

Paper Details

Authors:
Pengfei Zhu, Dongwei Ren, Hong Shi
Submitted On:
18 May 2020 - 11:23am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

BRN_slides.pdf

(26)

Subscribe

[1] Pengfei Zhu, Dongwei Ren, Hong Shi, "BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5393. Accessed: Aug. 12, 2020.
@article{5393-20,
url = {http://sigport.org/5393},
author = {Pengfei Zhu; Dongwei Ren; Hong Shi },
publisher = {IEEE SigPort},
title = {BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING},
year = {2020} }
TY - EJOUR
T1 - BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING
AU - Pengfei Zhu; Dongwei Ren; Hong Shi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5393
ER -
Pengfei Zhu, Dongwei Ren, Hong Shi. (2020). BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING. IEEE SigPort. http://sigport.org/5393
Pengfei Zhu, Dongwei Ren, Hong Shi, 2020. BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING. Available at: http://sigport.org/5393.
Pengfei Zhu, Dongwei Ren, Hong Shi. (2020). "BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING." Web.
1. Pengfei Zhu, Dongwei Ren, Hong Shi. BILATERAL RECURRENT NETWORK FOR SINGLE IMAGE DERAINING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5393

Pages