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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.

Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations

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Authors:
Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan
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17 May 2020 - 3:10pm
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ICASSP 2020 presentation

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[1] Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan, "Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5385. Accessed: Jun. 06, 2020.
@article{5385-20,
url = {http://sigport.org/5385},
author = {Jiaxi Wang; Karel Mundnich; Allison T. Knoll; Pat Levitt; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations},
year = {2020} }
TY - EJOUR
T1 - Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations
AU - Jiaxi Wang; Karel Mundnich; Allison T. Knoll; Pat Levitt; Shrikanth Narayanan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5385
ER -
Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan. (2020). Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations. IEEE SigPort. http://sigport.org/5385
Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan, 2020. Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations. Available at: http://sigport.org/5385.
Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan. (2020). "Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations." Web.
1. Jiaxi Wang, Karel Mundnich, Allison T. Knoll, Pat Levitt, Shrikanth Narayanan. Bringing in the outliers: A sparse subspace clustering approach to learn a dictionary of mouse ultrasonic vocalizations [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5385

Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors


Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it’s power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and learns its own policy without considering others’ policies is a simple way to apply DRL to multi-agent tasks. However, since agents’ policies change as learning proceeds, from the perspective of each agent, the environment is non-stationary, which makes conventional DRL methods inefficient.

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Authors:
Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang
Submitted On:
17 May 2020 - 8:31am
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stabilizing_madrl_by_implicitly_estimating_other_agents'_behaviors.pdf

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[1] Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang, "Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5384. Accessed: Jun. 06, 2020.
@article{5384-20,
url = {http://sigport.org/5384},
author = {Yue Jin; Shuangqing Wei; Jian Yuan; Xudong Zhang; Chao Wang },
publisher = {IEEE SigPort},
title = {Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors},
year = {2020} }
TY - EJOUR
T1 - Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors
AU - Yue Jin; Shuangqing Wei; Jian Yuan; Xudong Zhang; Chao Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5384
ER -
Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang. (2020). Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors. IEEE SigPort. http://sigport.org/5384
Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang, 2020. Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors. Available at: http://sigport.org/5384.
Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang. (2020). "Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors." Web.
1. Yue Jin, Shuangqing Wei, Jian Yuan, Xudong Zhang, Chao Wang. Stabilizing Multi agent Deep Reinforcement Learning by Implicitly Estimating Other Agents’ Behaviors [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5384

Synchronous Transformers for End-to-End Speech Recognition

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Authors:
Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen
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17 May 2020 - 3:20am
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Sync-Transformer-icassp2020.pdf

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[1] Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen, "Synchronous Transformers for End-to-End Speech Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5382. Accessed: Jun. 06, 2020.
@article{5382-20,
url = {http://sigport.org/5382},
author = {Zhengkun Tian; Jiangyan Yi; Ye Bai; Jianhua Tao; Shuai Zhang; Zhengqi Wen },
publisher = {IEEE SigPort},
title = {Synchronous Transformers for End-to-End Speech Recognition},
year = {2020} }
TY - EJOUR
T1 - Synchronous Transformers for End-to-End Speech Recognition
AU - Zhengkun Tian; Jiangyan Yi; Ye Bai; Jianhua Tao; Shuai Zhang; Zhengqi Wen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5382
ER -
Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen. (2020). Synchronous Transformers for End-to-End Speech Recognition. IEEE SigPort. http://sigport.org/5382
Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen, 2020. Synchronous Transformers for End-to-End Speech Recognition. Available at: http://sigport.org/5382.
Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen. (2020). "Synchronous Transformers for End-to-End Speech Recognition." Web.
1. Zhengkun Tian, Jiangyan Yi, Ye Bai, Jianhua Tao, Shuai Zhang, Zhengqi Wen. Synchronous Transformers for End-to-End Speech Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5382

Experiments in Creating Online Course Content for Signal Processing Education


A brief introduction to the NPTEL.ac.in platform of India which
provides free access to quality online educational content for
Signal Processing. Experiences of creating courses related to Signal Processing,
supported by the European Union-funded project, MIELES.

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Authors:
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan
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16 May 2020 - 10:14pm
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TH3.PD-2914-ICASSP2020-KVS-HARI-Presentation.pdf

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[1] C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan, "Experiments in Creating Online Course Content for Signal Processing Education", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5381. Accessed: Jun. 06, 2020.
@article{5381-20,
url = {http://sigport.org/5381},
author = {C.-G. Jansson; R. Thottappillil; S. Hillmann; S. Möller; K V S Hari; R. Sundaresan },
publisher = {IEEE SigPort},
title = {Experiments in Creating Online Course Content for Signal Processing Education},
year = {2020} }
TY - EJOUR
T1 - Experiments in Creating Online Course Content for Signal Processing Education
AU - C.-G. Jansson; R. Thottappillil; S. Hillmann; S. Möller; K V S Hari; R. Sundaresan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5381
ER -
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. (2020). Experiments in Creating Online Course Content for Signal Processing Education. IEEE SigPort. http://sigport.org/5381
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan, 2020. Experiments in Creating Online Course Content for Signal Processing Education. Available at: http://sigport.org/5381.
C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. (2020). "Experiments in Creating Online Course Content for Signal Processing Education." Web.
1. C.-G. Jansson, R. Thottappillil, S. Hillmann, S. Möller, K V S Hari, R. Sundaresan. Experiments in Creating Online Course Content for Signal Processing Education [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5381

Adversarial Networks for Secure Wireless Communications - Slides


We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit.

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Authors:
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz
Submitted On:
16 May 2020 - 4:33pm
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marchiorot-slides.pdf

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[1] Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, "Adversarial Networks for Secure Wireless Communications - Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5380. Accessed: Jun. 06, 2020.
@article{5380-20,
url = {http://sigport.org/5380},
author = {Thomas Marchioro; Nicola Laurenti; Deniz Gunduz },
publisher = {IEEE SigPort},
title = {Adversarial Networks for Secure Wireless Communications - Slides},
year = {2020} }
TY - EJOUR
T1 - Adversarial Networks for Secure Wireless Communications - Slides
AU - Thomas Marchioro; Nicola Laurenti; Deniz Gunduz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5380
ER -
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). Adversarial Networks for Secure Wireless Communications - Slides. IEEE SigPort. http://sigport.org/5380
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, 2020. Adversarial Networks for Secure Wireless Communications - Slides. Available at: http://sigport.org/5380.
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). "Adversarial Networks for Secure Wireless Communications - Slides." Web.
1. Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. Adversarial Networks for Secure Wireless Communications - Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5380

UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS


We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high-quality signals that minimize a target objective function. To efficiently quantize the compressed signal, non-uniformly quantized optimal latent vectors are identified by iterative back-propagation with ADMM optimization performed for each iteration.

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16 May 2020 - 4:30pm
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4405.pdf

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[1] , "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5379. Accessed: Jun. 06, 2020.
@article{5379-20,
url = {http://sigport.org/5379},
author = { },
publisher = {IEEE SigPort},
title = {UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS},
year = {2020} }
TY - EJOUR
T1 - UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5379
ER -
. (2020). UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. IEEE SigPort. http://sigport.org/5379
, 2020. UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS. Available at: http://sigport.org/5379.
. (2020). "UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS." Web.
1. . UNIFIED SIGNAL COMPRESSION USING GENERATIVE ADVERSARIAL NETWORKS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5379

EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK


In speech production, epochs are glottal closure instants where significant energy is released from the lungs. Extracting an epoch accurately is important in speech synthesis, analysis, and pitch oriented studies. The time-varying characteristics of the source and the system, and channel attenuation of low-frequency components by telephone channels make estimation of epoch from a speech signal a challenging task.

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Authors:
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula
Submitted On:
16 May 2020 - 2:22pm
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Epoch Extraction using gammatone wavelets

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[1] Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula, "EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5378. Accessed: Jun. 06, 2020.
@article{5378-20,
url = {http://sigport.org/5378},
author = {Pavan Kulkarni; Jishnu Sadasivan; Aniruddha Adiga; Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK},
year = {2020} }
TY - EJOUR
T1 - EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK
AU - Pavan Kulkarni; Jishnu Sadasivan; Aniruddha Adiga; Chandra Sekhar Seelamantula
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5378
ER -
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. (2020). EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK. IEEE SigPort. http://sigport.org/5378
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula, 2020. EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK. Available at: http://sigport.org/5378.
Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. (2020). "EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK." Web.
1. Pavan Kulkarni, Jishnu Sadasivan, Aniruddha Adiga, Chandra Sekhar Seelamantula. EPOCH EXTRACTION FROM A SPEECH SIGNAL USING GAMMATONE WAVELETS IN A SCATTERING NETWORK [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5378

Embedded Large–Scale Handwritten Chinese Character Recognition


As handwriting input becomes more prevalent, the large symbol inventory required to support Chinese handwriting recognition poses unique challenges. This paper describes how the Apple deep learning recognition system can accurately handle up to 30,000 Chinese characters while running in real-time across a range of mobile devices.

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Authors:
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda
Submitted On:
16 May 2020 - 11:46am
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_Embedded Large Scale Handwritten Chinese Character.pdf

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[1] Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, "Embedded Large–Scale Handwritten Chinese Character Recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5377. Accessed: Jun. 06, 2020.
@article{5377-20,
url = {http://sigport.org/5377},
author = {Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda },
publisher = {IEEE SigPort},
title = {Embedded Large–Scale Handwritten Chinese Character Recognition},
year = {2020} }
TY - EJOUR
T1 - Embedded Large–Scale Handwritten Chinese Character Recognition
AU - Hans J. G. A. Dolfing; Ryan S. Dixon; Jerome R. Bellegarda
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5377
ER -
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). Embedded Large–Scale Handwritten Chinese Character Recognition. IEEE SigPort. http://sigport.org/5377
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda, 2020. Embedded Large–Scale Handwritten Chinese Character Recognition. Available at: http://sigport.org/5377.
Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. (2020). "Embedded Large–Scale Handwritten Chinese Character Recognition." Web.
1. Hans J. G. A. Dolfing, Ryan S. Dixon, Jerome R. Bellegarda. Embedded Large–Scale Handwritten Chinese Character Recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5377

MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING

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Authors:
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
Submitted On:
16 May 2020 - 11:32am
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ICASSP_PPT20.pdf

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[1] Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia, "MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5376. Accessed: Jun. 06, 2020.
@article{5376-20,
url = {http://sigport.org/5376},
author = {Shikha Singh; Jyoti Maggu; Angshul Majumdar; Emilie Chouzenoux; Giovanni Chierchia },
publisher = {IEEE SigPort},
title = {MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING},
year = {2020} }
TY - EJOUR
T1 - MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING
AU - Shikha Singh; Jyoti Maggu; Angshul Majumdar; Emilie Chouzenoux; Giovanni Chierchia
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5376
ER -
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. (2020). MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING. IEEE SigPort. http://sigport.org/5376
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia, 2020. MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING. Available at: http://sigport.org/5376.
Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. (2020). "MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING." Web.
1. Shikha Singh, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia. MULTI-LABEL CONSISTENT CONVOLUTIONAL TRANSFORM LEARNING: APPLICATION TO NON-INTRUSIVE LOAD MONITORING [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5376

Automatic identification of speakers from head gestures in a narration


In this work, we focus on quantifying speaker identity information encoded in the head gestures of speakers, while they narrate a story. We hypothesize that the head gestures over a long duration have speaker-specific patterns. To establish this, we consider a classification problem to identify speakers from head gestures. We represent every head orientation as a triplet of Euler angles and a sequence of head orientations as head gestures.

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Authors:
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh
Submitted On:
16 May 2020 - 10:52am
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ICASSP_2020_Presentation.pdf

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[1] Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh, "Automatic identification of speakers from head gestures in a narration", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5375. Accessed: Jun. 06, 2020.
@article{5375-20,
url = {http://sigport.org/5375},
author = {Sanjeev Kadagathur Vadiraj; Achuth Rao M V; Prasanta Kumar Ghosh },
publisher = {IEEE SigPort},
title = {Automatic identification of speakers from head gestures in a narration},
year = {2020} }
TY - EJOUR
T1 - Automatic identification of speakers from head gestures in a narration
AU - Sanjeev Kadagathur Vadiraj; Achuth Rao M V; Prasanta Kumar Ghosh
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5375
ER -
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. (2020). Automatic identification of speakers from head gestures in a narration. IEEE SigPort. http://sigport.org/5375
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh, 2020. Automatic identification of speakers from head gestures in a narration. Available at: http://sigport.org/5375.
Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. (2020). "Automatic identification of speakers from head gestures in a narration." Web.
1. Sanjeev Kadagathur Vadiraj, Achuth Rao M V, Prasanta Kumar Ghosh. Automatic identification of speakers from head gestures in a narration [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5375

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