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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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

ACTIVE LEARNING WITH LABEL PROPORTIONS


Active Learning (AL) refers to the setting where the learner has the ability to perform queries to an oracle to acquire the true label of an instance or, sometimes, a set of instances. Even though Active Learning has been studied extensively, the setting is usually restricted to assume that the oracle is trustworthy and will provide the actual label. We argue that, while common, this approach can be made more flexible to account for different forms of supervision.

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Authors:
Raul Santos-Rodriguez, Niall Twomey
Submitted On:
1 March 2019 - 1:22pm
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[1] Raul Santos-Rodriguez, Niall Twomey, "ACTIVE LEARNING WITH LABEL PROPORTIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3855. Accessed: Mar. 23, 2019.
@article{3855-19,
url = {http://sigport.org/3855},
author = {Raul Santos-Rodriguez; Niall Twomey },
publisher = {IEEE SigPort},
title = {ACTIVE LEARNING WITH LABEL PROPORTIONS},
year = {2019} }
TY - EJOUR
T1 - ACTIVE LEARNING WITH LABEL PROPORTIONS
AU - Raul Santos-Rodriguez; Niall Twomey
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3855
ER -
Raul Santos-Rodriguez, Niall Twomey. (2019). ACTIVE LEARNING WITH LABEL PROPORTIONS. IEEE SigPort. http://sigport.org/3855
Raul Santos-Rodriguez, Niall Twomey, 2019. ACTIVE LEARNING WITH LABEL PROPORTIONS. Available at: http://sigport.org/3855.
Raul Santos-Rodriguez, Niall Twomey. (2019). "ACTIVE LEARNING WITH LABEL PROPORTIONS." Web.
1. Raul Santos-Rodriguez, Niall Twomey. ACTIVE LEARNING WITH LABEL PROPORTIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3855

DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION


Most covariance-based representations of actions are focused on the statistical features of poses by empirical averaging weighting. Note that these poses have a variety of saliency levels for different actions. Neglecting pose saliency could degrade the discriminative power of the covariance features, and further reduce the performance of action recognition. In this paper, we propose a novel saliency weighting covariance feature representation, Saliency-Pose-Attention Covariance(SPA-Cov), which reduces the negative effects from the ambiguous pose samples.

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Authors:
Zhiyong Feng,Yong Su,Meng Xing
Submitted On:
18 February 2019 - 6:34am
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[1] Zhiyong Feng,Yong Su,Meng Xing, "DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3854. Accessed: Mar. 23, 2019.
@article{3854-19,
url = {http://sigport.org/3854},
author = {Zhiyong Feng;Yong Su;Meng Xing },
publisher = {IEEE SigPort},
title = {DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION
AU - Zhiyong Feng;Yong Su;Meng Xing
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3854
ER -
Zhiyong Feng,Yong Su,Meng Xing. (2019). DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION. IEEE SigPort. http://sigport.org/3854
Zhiyong Feng,Yong Su,Meng Xing, 2019. DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION. Available at: http://sigport.org/3854.
Zhiyong Feng,Yong Su,Meng Xing. (2019). "DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION." Web.
1. Zhiyong Feng,Yong Su,Meng Xing. DISCRIMINATIVE SALIENCY-POSE-ATTENTION COVARIANCE FOR ACTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3854

SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY


Internet-of-Things (IoT) networks are envisioned to typically
include a massive number of devices with sporadic and low-latency
uplink service needs. This paper presents a blind
demixing approach to support the data recovery of multiple
simultaneous and unscheduled device transmissions without
a priori channel state information (CSI). The proposed joint
receiver leverages the group sparse bilinear characteristics
of the underlying problem that involves active device detection
and data recovery. We exploit the manifold geometry

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Authors:
Yuanming Shi, Zhi Ding
Submitted On:
16 February 2019 - 9:46pm
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[1] Yuanming Shi, Zhi Ding, "SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3853. Accessed: Mar. 23, 2019.
@article{3853-19,
url = {http://sigport.org/3853},
author = {Yuanming Shi; Zhi Ding },
publisher = {IEEE SigPort},
title = {SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY},
year = {2019} }
TY - EJOUR
T1 - SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY
AU - Yuanming Shi; Zhi Ding
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3853
ER -
Yuanming Shi, Zhi Ding. (2019). SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY. IEEE SigPort. http://sigport.org/3853
Yuanming Shi, Zhi Ding, 2019. SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY. Available at: http://sigport.org/3853.
Yuanming Shi, Zhi Ding. (2019). "SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY." Web.
1. Yuanming Shi, Zhi Ding. SPARSE BLIND DEMIXING FOR LOW-LATENCY SIGNAL RECOVERY IN MASSIVE CONNECTIVITY [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3853

DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS


Deep neural networks (DNNs) have been successfully deployed for acoustic modelling in statistical parametric speech synthesis (SPSS) systems. Moreover, DNN-based postfilters (PF) have also been shown to outperform conventional postfilters that are widely used in SPSS systems for increasing the quality of synthesized speech. However, existing DNN-based postfilters are trained with speaker-dependent databases. Given that SPSS systems can rapidly adapt to new speakers from generic models, there is a need for DNN-based postfilters that can adapt to new speakers with minimal adaptation data.

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Authors:
Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu
Submitted On:
15 February 2019 - 5:17am
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[1] Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu, "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3851. Accessed: Mar. 23, 2019.
@article{3851-19,
url = {http://sigport.org/3851},
author = {Miraç Göksu Öztürk;Okan Ulusoy;Cenk Demiroglu },
publisher = {IEEE SigPort},
title = {DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS},
year = {2019} }
TY - EJOUR
T1 - DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS
AU - Miraç Göksu Öztürk;Okan Ulusoy;Cenk Demiroglu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3851
ER -
Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu. (2019). DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. IEEE SigPort. http://sigport.org/3851
Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu, 2019. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS. Available at: http://sigport.org/3851.
Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu. (2019). "DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS." Web.
1. Miraç Göksu Öztürk,Okan Ulusoy,Cenk Demiroglu. DNN-BASED SPEAKER-ADAPTIVE POSTFILTERING WITH LIMITED ADAPTATION DATA FOR STATISTICAL SPEECH SYNTHESIS SYSTEMS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3851

Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game


This paper studies the problem of Stackelberg game based distributed power allocation for spectral coexisting multistatic radar and communication systems. The strategy aims to minimize the radiated power of each radar by optimizing transmit power allocation for a desired signal-to-interference-plus-noise ratio (SINR) meanwhile the communication base station (CBS) is protected from the interference of radar transmissions. We formulate this distributed power allocation process as a Stackelberg game, where the CBS is a leader and the radars are the followers.

Paper Details

Authors:
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou
Submitted On:
14 February 2019 - 10:01pm
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Poster_1027.pdf

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[1] Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou, "Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3850. Accessed: Mar. 23, 2019.
@article{3850-19,
url = {http://sigport.org/3850},
author = {Chenguang Shi; Fei Wang; Sana Salous; Jianjiang Zhou },
publisher = {IEEE SigPort},
title = {Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game},
year = {2019} }
TY - EJOUR
T1 - Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game
AU - Chenguang Shi; Fei Wang; Sana Salous; Jianjiang Zhou
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3850
ER -
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. (2019). Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game. IEEE SigPort. http://sigport.org/3850
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou, 2019. Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game. Available at: http://sigport.org/3850.
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. (2019). "Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game." Web.
1. Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3850