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Audio and Acoustic Signal Processing

ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS

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[1] , "ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3700. Accessed: Aug. 17, 2019.
@article{3700-18,
url = {http://sigport.org/3700},
author = { },
publisher = {IEEE SigPort},
title = {ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS},
year = {2018} }
TY - EJOUR
T1 - ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3700
ER -
. (2018). ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS. IEEE SigPort. http://sigport.org/3700
, 2018. ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS. Available at: http://sigport.org/3700.
. (2018). "ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS." Web.
1. . ELITE GRADIENT DESCENT OPTIMIZATION OF ANTENNA PARAMETERS CONSTRAINED BY RADIO COVERAGE IN GREEN CELLULAR NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3700

A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI


This paper determined the best combination that maximizes the classification accuracy of single-channel electroencephalogram (EEG)-based motor imagery brain–computer interfaces (BCIs). BCIs allow people including completely locked-in patients to communicate with others without actual movements of body. Whereas EEGs are usually observed by multiple electrodes, single-channel measurement has been recently studied for gaining the simplicity of use.

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Suguru Kanoga, Atsunori Kanemura, Hideki Asoh
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26 November 2018 - 2:17pm
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A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI.pdf

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[1] Suguru Kanoga, Atsunori Kanemura, Hideki Asoh, "A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3691. Accessed: Aug. 17, 2019.
@article{3691-18,
url = {http://sigport.org/3691},
author = {Suguru Kanoga; Atsunori Kanemura; Hideki Asoh },
publisher = {IEEE SigPort},
title = {A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI},
year = {2018} }
TY - EJOUR
T1 - A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI
AU - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3691
ER -
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. (2018). A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI. IEEE SigPort. http://sigport.org/3691
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh, 2018. A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI. Available at: http://sigport.org/3691.
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. (2018). "A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI." Web.
1. Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3691

A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI


This paper determined the best combination that maximizes the classification accuracy of single-channel electroencephalogram (EEG)-based motor imagery brain–computer interfaces (BCIs). BCIs allow people including completely locked-in patients to communicate with others without actual movements of body. Whereas EEGs are usually observed by multiple electrodes, single-channel measurement has been recently studied for gaining the simplicity of use.

Paper Details

Authors:
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh
Submitted On:
27 March 2019 - 9:05am
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A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI.pdf

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[1] Suguru Kanoga, Atsunori Kanemura, Hideki Asoh, "A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3689. Accessed: Aug. 17, 2019.
@article{3689-18,
url = {http://sigport.org/3689},
author = {Suguru Kanoga; Atsunori Kanemura; Hideki Asoh },
publisher = {IEEE SigPort},
title = {A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI},
year = {2018} }
TY - EJOUR
T1 - A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI
AU - Suguru Kanoga; Atsunori Kanemura; Hideki Asoh
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3689
ER -
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. (2018). A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI. IEEE SigPort. http://sigport.org/3689
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh, 2018. A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI. Available at: http://sigport.org/3689.
Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. (2018). "A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI." Web.
1. Suguru Kanoga, Atsunori Kanemura, Hideki Asoh. A COMPARATIVE STUDY OF FEATURES AND CLASSIFIERS IN SINGLE-CHANNEL EEG-BASED MOTOR IMAGERY BCI [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3689

What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance


Civic engagement platforms such as SeeClickFix and FixMyStreet have revolutionized the way citizens interact with local governments to report and resolve urban issues. However, recognizing which urban issues are important to the community in an accurate and timely manner is essential for authorities to prioritize important issues, allocate resources and maintain citizens’ satisfaction with local governments.

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Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis
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19 November 2018 - 3:29pm
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[1] Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis, "What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3688. Accessed: Aug. 17, 2019.
@article{3688-18,
url = {http://sigport.org/3688},
author = {Yasitha Liyanage; Mengfan Yao; Christopher Yong; Daphney-Stavroula Zois; Charalampos Chelmis },
publisher = {IEEE SigPort},
title = {What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance},
year = {2018} }
TY - EJOUR
T1 - What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance
AU - Yasitha Liyanage; Mengfan Yao; Christopher Yong; Daphney-Stavroula Zois; Charalampos Chelmis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3688
ER -
Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis. (2018). What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance. IEEE SigPort. http://sigport.org/3688
Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis, 2018. What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance. Available at: http://sigport.org/3688.
Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis. (2018). "What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance." Web.
1. Yasitha Liyanage, Mengfan Yao, Christopher Yong, Daphney-Stavroula Zois, Charalampos Chelmis. What matters the most? Optimal Quick Classification of Urban Issue Reports by Importance [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3688

Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions


Accurate traffic accident detection is crucial to improving road safety conditions and route navigation, and to making informed decisions in urban planning among others. This paper proposes a Bayesian quickest change detection approach for accurate freeway accident detection in near–real–time based on speed sensor readings. Since post–accident conditions are hardly known, a maximum likelihood method is devised to track the relevant unknown parameters over time. Four aggregation schemes are designed to exploit the spatial correlation among sensors.

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Authors:
Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis
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19 November 2018 - 3:29pm
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[1] Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis, "Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3687. Accessed: Aug. 17, 2019.
@article{3687-18,
url = {http://sigport.org/3687},
author = {Yasitha Liyanage; Daphney-Stavroula Zois; Charalampos Chelmis },
publisher = {IEEE SigPort},
title = {Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions},
year = {2018} }
TY - EJOUR
T1 - Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions
AU - Yasitha Liyanage; Daphney-Stavroula Zois; Charalampos Chelmis
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3687
ER -
Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis. (2018). Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions. IEEE SigPort. http://sigport.org/3687
Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis, 2018. Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions. Available at: http://sigport.org/3687.
Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis. (2018). "Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions." Web.
1. Yasitha Liyanage, Daphney-Stavroula Zois, Charalampos Chelmis. Quickest Freeway Accident Detection Under Unknown Post–Accident Conditions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3687

Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays


This work addresses the problem of Port-Starboard (PS) beamforming for low-frequency active sonar (LFAS) with a triplet receiver array.

The work presents a new algorithm for sub-bands beam-space adaptive beamforming with twist compensation and evaluates its performance with experimental data collected at sea.

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Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa
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17 November 2018 - 5:20am
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Adaptive Beamformer

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[1] Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa, "Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3672. Accessed: Aug. 17, 2019.
@article{3672-18,
url = {http://sigport.org/3672},
author = {Alessandra Tesei; Alain Maguer; Fabrizio Ferraioli; Valerio Latini; Luca Pesa },
publisher = {IEEE SigPort},
title = {Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays},
year = {2018} }
TY - EJOUR
T1 - Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays
AU - Alessandra Tesei; Alain Maguer; Fabrizio Ferraioli; Valerio Latini; Luca Pesa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3672
ER -
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. (2018). Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays. IEEE SigPort. http://sigport.org/3672
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa, 2018. Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays. Available at: http://sigport.org/3672.
Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. (2018). "Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays." Web.
1. Alessandra Tesei, Alain Maguer, Fabrizio Ferraioli, Valerio Latini, Luca Pesa. Sub-Bands Beam-Space Adaptive Beamformer for Port-Starboard Rejection in Triplet Sonar Arrays [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3672

Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition


Fine-grained recognition focuses on the challenging task of automatically identifying the subtle differences between similar categories. Current state-of-the-art approaches require elaborated feature learning procedures, involving tuning several hyper-parameters, or rely on expensive human annotations such as objects or parts location. In this paper we propose a simple method for fine-grained recognition that exploits a nearly cost-free attention-based focus operation to construct an ensemble of increasingly specialized Convolutional Neural Networks.

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Authors:
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'
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8 October 2018 - 9:25am
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[1] Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3632. Accessed: Aug. 17, 2019.
@article{3632-18,
url = {http://sigport.org/3632},
author = {Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo' },
publisher = {IEEE SigPort},
title = {Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition},
year = {2018} }
TY - EJOUR
T1 - Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition
AU - Andrea Simonelli; Stefano Messelodi; Francesco De Natale; Samuel Rota Bulo'
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3632
ER -
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. IEEE SigPort. http://sigport.org/3632
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo', 2018. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition. Available at: http://sigport.org/3632.
Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. (2018). "Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition." Web.
1. Andrea Simonelli, Stefano Messelodi, Francesco De Natale, Samuel Rota Bulo'. Increasingly specialized ensemble of Convolutional Neural Networks for Fine-grained recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3632

An Interior Point Method for Nonnegative Sparse Signal Reconstruction


We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.

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Authors:
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld
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7 October 2018 - 5:05pm
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[1] Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, "An Interior Point Method for Nonnegative Sparse Signal Reconstruction", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3603. Accessed: Aug. 17, 2019.
@article{3603-18,
url = {http://sigport.org/3603},
author = {Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld },
publisher = {IEEE SigPort},
title = {An Interior Point Method for Nonnegative Sparse Signal Reconstruction},
year = {2018} }
TY - EJOUR
T1 - An Interior Point Method for Nonnegative Sparse Signal Reconstruction
AU - Xiang Huang; Kuan He; Seunghwan Yoo; Oliver Cossairt; Aggelos Katsaggelos; Nicola Ferrier; and Mark Hereld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3603
ER -
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). An Interior Point Method for Nonnegative Sparse Signal Reconstruction. IEEE SigPort. http://sigport.org/3603
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld, 2018. An Interior Point Method for Nonnegative Sparse Signal Reconstruction. Available at: http://sigport.org/3603.
Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. (2018). "An Interior Point Method for Nonnegative Sparse Signal Reconstruction." Web.
1. Xiang Huang, Kuan He, Seunghwan Yoo, Oliver Cossairt, Aggelos Katsaggelos, Nicola Ferrier, and Mark Hereld. An Interior Point Method for Nonnegative Sparse Signal Reconstruction [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3603

An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging


There exist various types of information in retail food packages, including food product name, ingredients list and use by date. The correct recognition and coding of use by dates is especially critical in ensuring proper distribution of the product to the market and eliminating potential health risks caused by erroneous mislabelling. The latter can have a major negative effect on the health of consumers and consequently raise legal issues for suppliers.

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Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias
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4 October 2018 - 12:42pm
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[1] Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias, "An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3454. Accessed: Aug. 17, 2019.
@article{3454-18,
url = {http://sigport.org/3454},
author = {Fabio De Sousa Ribeiro; Liyun Gong; Francesco Caliva'; Mark Swainson; Kjartan Gudmundsson; Miao Yu; Georgios Leontidis; Xujiong Ye; Stefanos Kollias },
publisher = {IEEE SigPort},
title = {An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging},
year = {2018} }
TY - EJOUR
T1 - An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging
AU - Fabio De Sousa Ribeiro; Liyun Gong; Francesco Caliva'; Mark Swainson; Kjartan Gudmundsson; Miao Yu; Georgios Leontidis; Xujiong Ye; Stefanos Kollias
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3454
ER -
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. (2018). An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging. IEEE SigPort. http://sigport.org/3454
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias, 2018. An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging. Available at: http://sigport.org/3454.
Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. (2018). "An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging." Web.
1. Fabio De Sousa Ribeiro, Liyun Gong, Francesco Caliva', Mark Swainson, Kjartan Gudmundsson, Miao Yu, Georgios Leontidis, Xujiong Ye, Stefanos Kollias. An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3454

Sufficiency quantification for seamless text-independent speaker enrollment


Text-independent speaker recognition (TI-SR) requires a lengthy enrollment process that involves asking dedicated time from the user to create a reliable model of their voice. Seamless enrollment is a highly attractive feature which refers to the enrollment process that happens in the background and asks for no dedicated time from the user. One of the key problems in a fully automated seamless enrollment process is to determine the sufficiency of a given utterance collection for the purpose of TI-SR. No known metric exists in the literature to quantify sufficiency.

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Authors:
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal
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13 July 2018 - 3:38pm
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Poster presented at ICASSP 2018

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[1] Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal, "Sufficiency quantification for seamless text-independent speaker enrollment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3379. Accessed: Aug. 17, 2019.
@article{3379-18,
url = {http://sigport.org/3379},
author = {Gokcen Cilingir; Jonathan Huang; Mandar S Joshi; Narayan Biswal },
publisher = {IEEE SigPort},
title = {Sufficiency quantification for seamless text-independent speaker enrollment},
year = {2018} }
TY - EJOUR
T1 - Sufficiency quantification for seamless text-independent speaker enrollment
AU - Gokcen Cilingir; Jonathan Huang; Mandar S Joshi; Narayan Biswal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3379
ER -
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. (2018). Sufficiency quantification for seamless text-independent speaker enrollment. IEEE SigPort. http://sigport.org/3379
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal, 2018. Sufficiency quantification for seamless text-independent speaker enrollment. Available at: http://sigport.org/3379.
Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. (2018). "Sufficiency quantification for seamless text-independent speaker enrollment." Web.
1. Gokcen Cilingir, Jonathan Huang, Mandar S Joshi, Narayan Biswal. Sufficiency quantification for seamless text-independent speaker enrollment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3379

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