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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.

EnerGAN: A Generative Adversarial Network for Energy Disaggregation


An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned disaggregators, which produce more detailed load estimations for a specific appliance, compared to state of the art deep learning models. The Generator and Discriminator of the model are appropriately adapted to fit the particularities of NILM problem, whereas a Seeder component is added to provide encoded compact input vectors to the Generator.

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Authors:
Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis
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27 June 2020 - 2:06pm
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EnerGAN_ICASSP_2020_final.pdf

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[1] Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis, "EnerGAN: A Generative Adversarial Network for Energy Disaggregation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5463. Accessed: Sep. 23, 2020.
@article{5463-20,
url = {http://sigport.org/5463},
author = {Maria Kaselimi; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Anastasios Doulamis },
publisher = {IEEE SigPort},
title = {EnerGAN: A Generative Adversarial Network for Energy Disaggregation},
year = {2020} }
TY - EJOUR
T1 - EnerGAN: A Generative Adversarial Network for Energy Disaggregation
AU - Maria Kaselimi; Athanasios Voulodimos; Eftychios Protopapadakis; Nikolaos Doulamis; Anastasios Doulamis
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5463
ER -
Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis. (2020). EnerGAN: A Generative Adversarial Network for Energy Disaggregation. IEEE SigPort. http://sigport.org/5463
Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis, 2020. EnerGAN: A Generative Adversarial Network for Energy Disaggregation. Available at: http://sigport.org/5463.
Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis. (2020). "EnerGAN: A Generative Adversarial Network for Energy Disaggregation." Web.
1. Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios Doulamis. EnerGAN: A Generative Adversarial Network for Energy Disaggregation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5463

Learning to Fool the Speaker Recognition (poster)


Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention. Previous research mainly studied the attack to the vision-based system, such as fingerprint and face recognition. While the attack for speaker recognition has not been investigated yet, although it has been widely used in our daily life.

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9 June 2020 - 11:26am
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[1] , "Learning to Fool the Speaker Recognition (poster)", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5462. Accessed: Sep. 23, 2020.
@article{5462-20,
url = {http://sigport.org/5462},
author = { },
publisher = {IEEE SigPort},
title = {Learning to Fool the Speaker Recognition (poster)},
year = {2020} }
TY - EJOUR
T1 - Learning to Fool the Speaker Recognition (poster)
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5462
ER -
. (2020). Learning to Fool the Speaker Recognition (poster). IEEE SigPort. http://sigport.org/5462
, 2020. Learning to Fool the Speaker Recognition (poster). Available at: http://sigport.org/5462.
. (2020). "Learning to Fool the Speaker Recognition (poster)." Web.
1. . Learning to Fool the Speaker Recognition (poster) [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5462

dMazeRunner: Optimizing Convolutions on Dataflow Accelerators

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Authors:
Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee
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7 June 2020 - 8:48pm
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Dave2020ICASSP.pdf

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[1] Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee, "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5461. Accessed: Sep. 23, 2020.
@article{5461-20,
url = {http://sigport.org/5461},
author = {Shail Dave; Aviral Shrivastava; Youngbin Kim; Sasikanth Avancha; Kyoungwoo Lee },
publisher = {IEEE SigPort},
title = {dMazeRunner: Optimizing Convolutions on Dataflow Accelerators},
year = {2020} }
TY - EJOUR
T1 - dMazeRunner: Optimizing Convolutions on Dataflow Accelerators
AU - Shail Dave; Aviral Shrivastava; Youngbin Kim; Sasikanth Avancha; Kyoungwoo Lee
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5461
ER -
Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee. (2020). dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. IEEE SigPort. http://sigport.org/5461
Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee, 2020. dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. Available at: http://sigport.org/5461.
Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee. (2020). "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators." Web.
1. Shail Dave, Aviral Shrivastava, Youngbin Kim, Sasikanth Avancha, Kyoungwoo Lee. dMazeRunner: Optimizing Convolutions on Dataflow Accelerators [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5461

dMazeRunner: Optimizing Convolutions on Dataflow Accelerators

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7 June 2020 - 8:31pm
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Dave2020ICASSP.pdf

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[1] , "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5460. Accessed: Sep. 23, 2020.
@article{5460-20,
url = {http://sigport.org/5460},
author = { },
publisher = {IEEE SigPort},
title = {dMazeRunner: Optimizing Convolutions on Dataflow Accelerators},
year = {2020} }
TY - EJOUR
T1 - dMazeRunner: Optimizing Convolutions on Dataflow Accelerators
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5460
ER -
. (2020). dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. IEEE SigPort. http://sigport.org/5460
, 2020. dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. Available at: http://sigport.org/5460.
. (2020). "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators." Web.
1. . dMazeRunner: Optimizing Convolutions on Dataflow Accelerators [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5460

dMazeRunner: Optimizing Convolutions on Dataflow Accelerators

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Submitted On:
7 June 2020 - 8:31pm
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Dave2020ICASSP.pdf

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[1] , "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5459. Accessed: Sep. 23, 2020.
@article{5459-20,
url = {http://sigport.org/5459},
author = { },
publisher = {IEEE SigPort},
title = {dMazeRunner: Optimizing Convolutions on Dataflow Accelerators},
year = {2020} }
TY - EJOUR
T1 - dMazeRunner: Optimizing Convolutions on Dataflow Accelerators
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5459
ER -
. (2020). dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. IEEE SigPort. http://sigport.org/5459
, 2020. dMazeRunner: Optimizing Convolutions on Dataflow Accelerators. Available at: http://sigport.org/5459.
. (2020). "dMazeRunner: Optimizing Convolutions on Dataflow Accelerators." Web.
1. . dMazeRunner: Optimizing Convolutions on Dataflow Accelerators [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5459

Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides

Paper Details

Authors:
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux
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6 June 2020 - 10:30pm
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Libri-Light - A Benchmark for ASR with Limited or No Supervision -- ICASSP 2020.pdf

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[1] Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux, "Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5458. Accessed: Sep. 23, 2020.
@article{5458-20,
url = {http://sigport.org/5458},
author = {Morgan Rivière; Weiyi Zheng; Evgeny Kharitonov; Qiantong Xu; Pierre-Emmanuel Mazaré; Julien Karadayi; Vitaliy Liptchinsky; Ronan Collobert; Christian Fuegen; Tatiana Likhomanenko; Gabriel Synnaeve; Armand Joulin; Abdelrahman Mohamed; Emmanuel Dupoux },
publisher = {IEEE SigPort},
title = {Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides},
year = {2020} }
TY - EJOUR
T1 - Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides
AU - Morgan Rivière; Weiyi Zheng; Evgeny Kharitonov; Qiantong Xu; Pierre-Emmanuel Mazaré; Julien Karadayi; Vitaliy Liptchinsky; Ronan Collobert; Christian Fuegen; Tatiana Likhomanenko; Gabriel Synnaeve; Armand Joulin; Abdelrahman Mohamed; Emmanuel Dupoux
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5458
ER -
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. (2020). Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides. IEEE SigPort. http://sigport.org/5458
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux, 2020. Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides. Available at: http://sigport.org/5458.
Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. (2020). "Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides." Web.
1. Morgan Rivière, Weiyi Zheng, Evgeny Kharitonov, Qiantong Xu, Pierre-Emmanuel Mazaré, Julien Karadayi, Vitaliy Liptchinsky, Ronan Collobert, Christian Fuegen, Tatiana Likhomanenko, Gabriel Synnaeve, Armand Joulin, Abdelrahman Mohamed, Emmanuel Dupoux. Libri-Light: A Benchmark for ASR with Limited or No Supervision- ICASSP 2020 Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5458

Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides

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Ann Lee, Awni Hannun
Submitted On:
6 June 2020 - 10:19pm
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[1] Ann Lee, Awni Hannun, "Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5457. Accessed: Sep. 23, 2020.
@article{5457-20,
url = {http://sigport.org/5457},
author = { Ann Lee; Awni Hannun },
publisher = {IEEE SigPort},
title = {Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides},
year = {2020} }
TY - EJOUR
T1 - Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides
AU - Ann Lee; Awni Hannun
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5457
ER -
Ann Lee, Awni Hannun. (2020). Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides. IEEE SigPort. http://sigport.org/5457
Ann Lee, Awni Hannun, 2020. Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides. Available at: http://sigport.org/5457.
Ann Lee, Awni Hannun. (2020). "Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides." Web.
1. Ann Lee, Awni Hannun. Self-Training for End-to-End Speech Recognition - ICASSP 2020 Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5457

DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS


Accurate and efficient methods for Direction of Arrival (DOA) estimation play an important role in mmWave channel estimation methods. This estimation procedure can potentially be affected by the different RF and analog components in the communication system. Such components add an unknown, nonlinear distortion to the received signal. This work looks at addressing this problem of DOA estimation for a general case of a nonlinear distortion of the received signal. Two different scenarios for angle recovery are considered here: with the use of pilot symbols and without the use of pilots.

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5 June 2020 - 9:20pm
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ICASSP_Presentation_Aditya Sant_Handout.pdf

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[1] , "DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5456. Accessed: Sep. 23, 2020.
@article{5456-20,
url = {http://sigport.org/5456},
author = { },
publisher = {IEEE SigPort},
title = {DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS},
year = {2020} }
TY - EJOUR
T1 - DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5456
ER -
. (2020). DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS. IEEE SigPort. http://sigport.org/5456
, 2020. DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS. Available at: http://sigport.org/5456.
. (2020). "DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS." Web.
1. . DOA ESTIMATION IN SYSTEMS WITH NONLINEARITIES FOR MMWAVE COMMUNICATIONS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5456

Filtering out time-frequency areas using Gabor multipliers


We address the problem of filtering out localized time-frequency components in signals. The problem is formulatedas a minimization of a suitable quadratic form, that involves adata fidelity term on the short-time Fourier transform outsidethe support of the undesired component, and an energy pe-nalization term inside the support. The minimization yields alinear system whose solution can be expressed in closed formusing Gabor multipliers.

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5 June 2020 - 6:56am
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icassp_2020_Mkreme.pdf

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[1] , "Filtering out time-frequency areas using Gabor multipliers", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5455. Accessed: Sep. 23, 2020.
@article{5455-20,
url = {http://sigport.org/5455},
author = { },
publisher = {IEEE SigPort},
title = {Filtering out time-frequency areas using Gabor multipliers},
year = {2020} }
TY - EJOUR
T1 - Filtering out time-frequency areas using Gabor multipliers
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5455
ER -
. (2020). Filtering out time-frequency areas using Gabor multipliers. IEEE SigPort. http://sigport.org/5455
, 2020. Filtering out time-frequency areas using Gabor multipliers. Available at: http://sigport.org/5455.
. (2020). "Filtering out time-frequency areas using Gabor multipliers." Web.
1. . Filtering out time-frequency areas using Gabor multipliers [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5455

Differentiable Branching in Deep Networks for Fast Inference


In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning.

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Authors:
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini
Submitted On:
5 June 2020 - 4:28am
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Paper #1402_Presentation.pdf

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[1] Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini, "Differentiable Branching in Deep Networks for Fast Inference", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5453. Accessed: Sep. 23, 2020.
@article{5453-20,
url = {http://sigport.org/5453},
author = {Simone Scardapane; Danilo Comminiello; Michele Scarpiniti; Enzo Baccarelli; Aurelio Uncini },
publisher = {IEEE SigPort},
title = {Differentiable Branching in Deep Networks for Fast Inference},
year = {2020} }
TY - EJOUR
T1 - Differentiable Branching in Deep Networks for Fast Inference
AU - Simone Scardapane; Danilo Comminiello; Michele Scarpiniti; Enzo Baccarelli; Aurelio Uncini
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5453
ER -
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. (2020). Differentiable Branching in Deep Networks for Fast Inference. IEEE SigPort. http://sigport.org/5453
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini, 2020. Differentiable Branching in Deep Networks for Fast Inference. Available at: http://sigport.org/5453.
Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. (2020). "Differentiable Branching in Deep Networks for Fast Inference." Web.
1. Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Enzo Baccarelli, Aurelio Uncini. Differentiable Branching in Deep Networks for Fast Inference [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5453

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