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ICASSP 2018

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.

VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS


Video enhancement methods enable to optimize the viewing of video content at the end-user side. Most approaches do not consider the compressed nature of the available content. In the present work, we build upon a recently proposed video enhancement approach that explicitly models a compression stage. To apply the enhancement framework on compressed representations requires to extract specific syntax elements during their decoding. This additional information embeds the enhanced result in a domain that closely fits the observation.

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Authors:
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux
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12 April 2018 - 12:23pm
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poster presentation for video enhancement with convex optimization methods

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[1] Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux, "VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2430. Accessed: May. 19, 2019.
@article{2430-18,
url = {http://sigport.org/2430},
author = {Benoit Boyadjis; Andrei Purica; Beatrice Pesquet-Popescu; Frederic Dufaux },
publisher = {IEEE SigPort},
title = {VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS},
year = {2018} }
TY - EJOUR
T1 - VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS
AU - Benoit Boyadjis; Andrei Purica; Beatrice Pesquet-Popescu; Frederic Dufaux
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2430
ER -
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. (2018). VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS. IEEE SigPort. http://sigport.org/2430
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux, 2018. VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS. Available at: http://sigport.org/2430.
Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. (2018). "VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS." Web.
1. Benoit Boyadjis, Andrei Purica, Beatrice Pesquet-Popescu, Frederic Dufaux. VIDEO ENHANCEMENT WITH CONVEX OPTIMIZATION METHODS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2430

High Order Recurrent Neural Networks for Acoustic Modelling


Vanishing long-term gradients are a major issue in training standard recurrent neural networks (RNNs), which can be alleviated by long short-term memory (LSTM) models with memory cells. However, the extra parameters associated with the memory cells mean an LSTM layer has four times as many parameters as an RNN with the same hidden vector size. This paper addresses the vanishing gradient problem using a high order RNN (HORNN) which has additional connections from multiple previous time steps.

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Authors:
Chao Zhang, Phil Woodland
Submitted On:
12 April 2018 - 12:16pm
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[1] Chao Zhang, Phil Woodland, "High Order Recurrent Neural Networks for Acoustic Modelling", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2429. Accessed: May. 19, 2019.
@article{2429-18,
url = {http://sigport.org/2429},
author = {Chao Zhang; Phil Woodland },
publisher = {IEEE SigPort},
title = {High Order Recurrent Neural Networks for Acoustic Modelling},
year = {2018} }
TY - EJOUR
T1 - High Order Recurrent Neural Networks for Acoustic Modelling
AU - Chao Zhang; Phil Woodland
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2429
ER -
Chao Zhang, Phil Woodland. (2018). High Order Recurrent Neural Networks for Acoustic Modelling. IEEE SigPort. http://sigport.org/2429
Chao Zhang, Phil Woodland, 2018. High Order Recurrent Neural Networks for Acoustic Modelling. Available at: http://sigport.org/2429.
Chao Zhang, Phil Woodland. (2018). "High Order Recurrent Neural Networks for Acoustic Modelling." Web.
1. Chao Zhang, Phil Woodland. High Order Recurrent Neural Networks for Acoustic Modelling [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2429

Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals


Negative symptoms of schizophrenia are often associated with the blunting of emotional affect which creates a serious impediment in the daily functioning of the patients. Affective prosody is almost always adversely impacted in such cases, and is known to exhibit itself through the low-level acoustic signals of prosody. To automate and simplify the process of assessment of severity of emotion related symptoms of schizophrenia, we utilized these low-level acoustic signals to predict the expert subjective ratings assigned by a trained psychologist during an interview with the patient.

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Authors:
Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee
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12 April 2018 - 12:16pm
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[1] Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee, "Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2428. Accessed: May. 19, 2019.
@article{2428-18,
url = {http://sigport.org/2428},
author = {Debsubhra Chakrabortyy; Zixu Yang; Yasir Tahir; Tomasz Maszczyk; Justin Dauwels; Nadia Thalmann; Jianmin Zheng; Yogeswary Maniam; Nur Amirah; Bhing Leet Tan and Jimmy Lee },
publisher = {IEEE SigPort},
title = {Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals},
year = {2018} }
TY - EJOUR
T1 - Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals
AU - Debsubhra Chakrabortyy; Zixu Yang; Yasir Tahir; Tomasz Maszczyk; Justin Dauwels; Nadia Thalmann; Jianmin Zheng; Yogeswary Maniam; Nur Amirah; Bhing Leet Tan and Jimmy Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2428
ER -
Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee. (2018). Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals. IEEE SigPort. http://sigport.org/2428
Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee, 2018. Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals. Available at: http://sigport.org/2428.
Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee. (2018). "Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals." Web.
1. Debsubhra Chakrabortyy, Zixu Yang, Yasir Tahir, Tomasz Maszczyk, Justin Dauwels, Nadia Thalmann, Jianmin Zheng, Yogeswary Maniam, Nur Amirah, Bhing Leet Tan and Jimmy Lee. Prediction of Negative Symptoms of Schizophrenia from Emotion Related Low-Level Speech Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2428

ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS


The problem of sequential multiple hypothesis testing in a distributed sensor network is considered and two algorithms are proposed: the Consensus + Innovations Matrix Sequential Probability Ratio Test (CIMSPRT) for multiple simple hypotheses and the robust Least-Favorable-Density-CIMSPRT for hypotheses with uncertainties in the corresponding distributions. Simulations are performed to verify and evaluate the performance of both algorithms under different network conditions and noise contaminations.

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Authors:
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir
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12 April 2018 - 12:06pm
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Poster: ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS

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[1] Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir, "ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2427. Accessed: May. 19, 2019.
@article{2427-18,
url = {http://sigport.org/2427},
author = {Mark R. Leonard; Maximilian Stiefel; Michael Fauss; Abdelhak M. Zoubir },
publisher = {IEEE SigPort},
title = {ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS},
year = {2018} }
TY - EJOUR
T1 - ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS
AU - Mark R. Leonard; Maximilian Stiefel; Michael Fauss; Abdelhak M. Zoubir
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2427
ER -
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. (2018). ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS. IEEE SigPort. http://sigport.org/2427
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir, 2018. ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS. Available at: http://sigport.org/2427.
Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. (2018). "ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS." Web.
1. Mark R. Leonard, Maximilian Stiefel, Michael Fauss, Abdelhak M. Zoubir. ROBUST SEQUENTIAL TESTING OF MULTIPLE HYPOTHESES IN DISTRIBUTED SENSOR NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2427

A feature fusion method based on extreme learning machine for speech emotion recognition


Speech emotion recognition is important to understand users' intention in human-computer interaction. However, it is a challenging task partly because we cannot clearly know which feature and model are effective to distinguish emotions. Previous studies utilize convolutional neural network (CNN) directly on spectrograms to extract features, and bidirectional long short term memory (BLSTM) is the state-of-the-art model. However, there are two problems of CNN-BLSTM. Firstly, it doesn't utilize heuristic features based on priori knowledge.

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Authors:
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan
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12 April 2018 - 12:07pm
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[1] Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan, "A feature fusion method based on extreme learning machine for speech emotion recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2426. Accessed: May. 19, 2019.
@article{2426-18,
url = {http://sigport.org/2426},
author = {Longbiao Wang; Jianwu Dang; Linjuan Zhang; Haotian Guan },
publisher = {IEEE SigPort},
title = {A feature fusion method based on extreme learning machine for speech emotion recognition},
year = {2018} }
TY - EJOUR
T1 - A feature fusion method based on extreme learning machine for speech emotion recognition
AU - Longbiao Wang; Jianwu Dang; Linjuan Zhang; Haotian Guan
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2426
ER -
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. (2018). A feature fusion method based on extreme learning machine for speech emotion recognition. IEEE SigPort. http://sigport.org/2426
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan, 2018. A feature fusion method based on extreme learning machine for speech emotion recognition. Available at: http://sigport.org/2426.
Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. (2018). "A feature fusion method based on extreme learning machine for speech emotion recognition." Web.
1. Longbiao Wang, Jianwu Dang, Linjuan Zhang, Haotian Guan. A feature fusion method based on extreme learning machine for speech emotion recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2426

MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH

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Authors:
Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak
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18 April 2018 - 4:08pm
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[1] Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak, "MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2425. Accessed: May. 19, 2019.
@article{2425-18,
url = {http://sigport.org/2425},
author = {Nanxin Chen; Jesus Villalba; Yishay Carmiel; Najim Dehak },
publisher = {IEEE SigPort},
title = {MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH},
year = {2018} }
TY - EJOUR
T1 - MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH
AU - Nanxin Chen; Jesus Villalba; Yishay Carmiel; Najim Dehak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2425
ER -
Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak. (2018). MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH. IEEE SigPort. http://sigport.org/2425
Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak, 2018. MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH. Available at: http://sigport.org/2425.
Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak. (2018). "MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH." Web.
1. Nanxin Chen, Jesus Villalba, Yishay Carmiel, Najim Dehak. MEASURING UNCERTAINTY IN DEEP REGRESSION MODELS: THE CASE OF AGE ESTIMATION FROM SPEECH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2425

Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu


For about 10 years, detecting the presence of a secret message hidden
in an image was performed with an Ensemble Classifier trained
with Rich features. In recent years, studies such as Xu et al. have
indicated that well-designed Convolutional Neural Networks(CNN)
can achieve comparable performance to the two-step machine learning
approaches.
In this paper we propose a CNN that outperforms the state-ofthe-
art in terms of error probability. The proposition is in the continuity
of what has been recently proposed and it is a clever fusion

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Authors:
Frédéric COMBY , Marc CHAUMONT
Submitted On:
12 April 2018 - 11:57am
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[1] Frédéric COMBY , Marc CHAUMONT, "Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2424. Accessed: May. 19, 2019.
@article{2424-18,
url = {http://sigport.org/2424},
author = {Frédéric COMBY ; Marc CHAUMONT },
publisher = {IEEE SigPort},
title = {Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu},
year = {2018} }
TY - EJOUR
T1 - Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu
AU - Frédéric COMBY ; Marc CHAUMONT
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2424
ER -
Frédéric COMBY , Marc CHAUMONT. (2018). Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu. IEEE SigPort. http://sigport.org/2424
Frédéric COMBY , Marc CHAUMONT, 2018. Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu. Available at: http://sigport.org/2424.
Frédéric COMBY , Marc CHAUMONT. (2018). "Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu." Web.
1. Frédéric COMBY , Marc CHAUMONT. Yedrouj-Net: An efficient CNN for spatial steganalysis Results Conclusions • An efficient approach based on deep learning (CNN) for steganalysis. • Our method outperforms the state-of-the-art and others CNN-based models with and without taking extra measu [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2424

SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION

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Authors:
Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee
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12 April 2018 - 11:56am
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[1] Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee, "SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2423. Accessed: May. 19, 2019.
@article{2423-18,
url = {http://sigport.org/2423},
author = {Yu-Hsuan Wang; Hung-yi Lee; Lin-shan Lee },
publisher = {IEEE SigPort},
title = {SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION},
year = {2018} }
TY - EJOUR
T1 - SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION
AU - Yu-Hsuan Wang; Hung-yi Lee; Lin-shan Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2423
ER -
Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee. (2018). SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION. IEEE SigPort. http://sigport.org/2423
Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee, 2018. SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION. Available at: http://sigport.org/2423.
Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee. (2018). "SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION." Web.
1. Yu-Hsuan Wang, Hung-yi Lee, Lin-shan Lee. SEGMENTAL AUDIO WORD2VEC: REPRESENTING UTTERANCES AS SEQUENCES OF VECTORS WITH APPLICATIONS IN SPOKEN TERM DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2423

FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC

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Authors:
Hamza Hamout, Abderrahmane Elyousfi
Submitted On:
12 April 2018 - 11:56am
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[1] Hamza Hamout, Abderrahmane Elyousfi, "FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2422. Accessed: May. 19, 2019.
@article{2422-18,
url = {http://sigport.org/2422},
author = {Hamza Hamout; Abderrahmane Elyousfi },
publisher = {IEEE SigPort},
title = {FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC},
year = {2018} }
TY - EJOUR
T1 - FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC
AU - Hamza Hamout; Abderrahmane Elyousfi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2422
ER -
Hamza Hamout, Abderrahmane Elyousfi. (2018). FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC. IEEE SigPort. http://sigport.org/2422
Hamza Hamout, Abderrahmane Elyousfi, 2018. FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC. Available at: http://sigport.org/2422.
Hamza Hamout, Abderrahmane Elyousfi. (2018). "FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC." Web.
1. Hamza Hamout, Abderrahmane Elyousfi. FAST TEXTURE INTRA SIZE CODING BASED ON BIG DATA CLUSTERING FOR 3D-HEVC [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2422

Speaker Diarization with LSTM


For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior speaker verification performance. In this paper, we build on the success of d-vector based speaker verification systems to develop a new d-vector based approach to speaker diarization.

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Authors:
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno
Submitted On:
12 April 2018 - 11:54am
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[1] Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno, "Speaker Diarization with LSTM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2421. Accessed: May. 19, 2019.
@article{2421-18,
url = {http://sigport.org/2421},
author = {Quan Wang; Carlton Downey; Li Wan; Philip Andrew Mansfield; Ignacio Lopez Moreno },
publisher = {IEEE SigPort},
title = {Speaker Diarization with LSTM},
year = {2018} }
TY - EJOUR
T1 - Speaker Diarization with LSTM
AU - Quan Wang; Carlton Downey; Li Wan; Philip Andrew Mansfield; Ignacio Lopez Moreno
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2421
ER -
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. (2018). Speaker Diarization with LSTM. IEEE SigPort. http://sigport.org/2421
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno, 2018. Speaker Diarization with LSTM. Available at: http://sigport.org/2421.
Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. (2018). "Speaker Diarization with LSTM." Web.
1. Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno. Speaker Diarization with LSTM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2421

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