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ICASSP 2018

ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2018 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics. Visit ICASSP 2018.

Autoencoder-based image compression: can the learning be quantization independent?

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Authors:
Aline Roumy, Christine Guillemot
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20 April 2018 - 3:18pm
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presentation_icassp_2018.pdf

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[1] Aline Roumy, Christine Guillemot, "Autoencoder-based image compression: can the learning be quantization independent?", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3112. Accessed: Apr. 21, 2018.
@article{3112-18,
url = {http://sigport.org/3112},
author = {Aline Roumy; Christine Guillemot },
publisher = {IEEE SigPort},
title = {Autoencoder-based image compression: can the learning be quantization independent?},
year = {2018} }
TY - EJOUR
T1 - Autoencoder-based image compression: can the learning be quantization independent?
AU - Aline Roumy; Christine Guillemot
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3112
ER -
Aline Roumy, Christine Guillemot. (2018). Autoencoder-based image compression: can the learning be quantization independent?. IEEE SigPort. http://sigport.org/3112
Aline Roumy, Christine Guillemot, 2018. Autoencoder-based image compression: can the learning be quantization independent?. Available at: http://sigport.org/3112.
Aline Roumy, Christine Guillemot. (2018). "Autoencoder-based image compression: can the learning be quantization independent?." Web.
1. Aline Roumy, Christine Guillemot. Autoencoder-based image compression: can the learning be quantization independent? [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3112

Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning


This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completion dialogue systems. Inspired by generative adversarial networks (GAN), we train a discriminator to differentiate responses/actions generated by dialogue agents from responses/actions by experts.

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Authors:
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
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20 April 2018 - 12:23pm
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poster_icassp2018_v2.pptx

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[1] Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3110. Accessed: Apr. 21, 2018.
@article{3110-18,
url = {http://sigport.org/3110},
author = {Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong },
publisher = {IEEE SigPort},
title = {Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning},
year = {2018} }
TY - EJOUR
T1 - Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
AU - Baolin Peng; Xiujun Li; Jianfeng Gao; Jingjing Liu; Yun-Nung Chen; Kam-Fai Wong
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3110
ER -
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. IEEE SigPort. http://sigport.org/3110
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong, 2018. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning. Available at: http://sigport.org/3110.
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. (2018). "Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning." Web.
1. Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong. Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3110

Acoustic Reflector Localization and Classification


The process of understanding acoustic properties of environments is important for several applications, such as spatial audio, augmented reality and source separation. In this paper, multichannel room impulse responses are recorded and transformed into their direction of arrival (DOA)-time domain, by employing a superdirective beamformer. This domain can be represented as a 2D image. Hence, a novel image processing method is proposed to analyze the DOA-time domain, and estimate the reflection times of arrival and DOAs. The main acoustically reflective objects are then localized.

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Authors:
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton
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20 April 2018 - 12:07pm
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Remaggietal_ICASSP2018.pdf

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[1] Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, "Acoustic Reflector Localization and Classification", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3109. Accessed: Apr. 21, 2018.
@article{3109-18,
url = {http://sigport.org/3109},
author = {Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton },
publisher = {IEEE SigPort},
title = {Acoustic Reflector Localization and Classification},
year = {2018} }
TY - EJOUR
T1 - Acoustic Reflector Localization and Classification
AU - Luca Remaggi; Hansung Kim; Philip J. B. Jackson; Filippo M. Fazi; Adrian Hilton
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3109
ER -
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). Acoustic Reflector Localization and Classification. IEEE SigPort. http://sigport.org/3109
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton, 2018. Acoustic Reflector Localization and Classification. Available at: http://sigport.org/3109.
Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. (2018). "Acoustic Reflector Localization and Classification." Web.
1. Luca Remaggi, Hansung Kim, Philip J. B. Jackson, Filippo M. Fazi, Adrian Hilton. Acoustic Reflector Localization and Classification [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3109

OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS


Detecting and localizing anomalies in surveillance videos is an ongoing challenge. Most existing methods are patch or trajectory-based, which lack semantic understanding of scenes and may split targets into pieces. To handle this prob-lem, this paper proposes a novel and effective algorithm by incorporating deep object detection and tracking with full utilization of spatial and temporal information.

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Authors:
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu
Submitted On:
20 April 2018 - 10:55am
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ICASSP2018-2394.pdf

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[1] Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu, "OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3108. Accessed: Apr. 21, 2018.
@article{3108-18,
url = {http://sigport.org/3108},
author = {Xiaodan Li; Weihai Li; Bin Liu; Qiankun Liu; Nenghai Yu },
publisher = {IEEE SigPort},
title = {OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS},
year = {2018} }
TY - EJOUR
T1 - OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS
AU - Xiaodan Li; Weihai Li; Bin Liu; Qiankun Liu; Nenghai Yu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3108
ER -
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. (2018). OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS. IEEE SigPort. http://sigport.org/3108
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu, 2018. OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS. Available at: http://sigport.org/3108.
Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. (2018). "OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS." Web.
1. Xiaodan Li, Weihai Li, Bin Liu, Qiankun Liu, Nenghai Yu. OBJECT-ORIENTED ANOMALY DETECTION IN SURVEILLANCE VIDEOS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3108

RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network


Video change detection, which plays an important role in computer vision, is far from being well resolved due to the complexity of diverse scenes in real world. Most of the current methods are designed based on hand-crafted features and perform well in some certain scenes but may fail on others. This paper puts up forward a deep learning based method to automatically fuse multiple basic detections into an optimal

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Submitted On:
20 April 2018 - 10:43am
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ICASSP2018_poster.pdf

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[1] , " RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3107. Accessed: Apr. 21, 2018.
@article{3107-18,
url = {http://sigport.org/3107},
author = { },
publisher = {IEEE SigPort},
title = { RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network},
year = {2018} }
TY - EJOUR
T1 - RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3107
ER -
. (2018). RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network. IEEE SigPort. http://sigport.org/3107
, 2018. RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network. Available at: http://sigport.org/3107.
. (2018). " RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network." Web.
1. . RCDFNN: Robust Change Detection based on Convolutional Fusion Neural Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3107

END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN

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20 April 2018 - 10:06am
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ICASSP_2018_koizumi_r03.pdf

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[1] , "END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3106. Accessed: Apr. 21, 2018.
@article{3106-18,
url = {http://sigport.org/3106},
author = { },
publisher = {IEEE SigPort},
title = {END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN},
year = {2018} }
TY - EJOUR
T1 - END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3106
ER -
. (2018). END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN. IEEE SigPort. http://sigport.org/3106
, 2018. END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN. Available at: http://sigport.org/3106.
. (2018). "END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN." Web.
1. . END-TO-END SOUND SOURCE ENHANCEMENT USING DEEP NEURAL NETWORK IN THE MODIFIED DISCRETE COSINE TRANSFORM DOMAIN [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3106

ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE

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Authors:
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen
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20 April 2018 - 9:23am
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ICASSP_2018.pdf

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[1] Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen, "ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3104. Accessed: Apr. 21, 2018.
@article{3104-18,
url = {http://sigport.org/3104},
author = {Xin Leng; Jingdong Chen; Jacob Benesty; Israel Cohen },
publisher = {IEEE SigPort},
title = {ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE},
year = {2018} }
TY - EJOUR
T1 - ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE
AU - Xin Leng; Jingdong Chen; Jacob Benesty; Israel Cohen
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3104
ER -
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. (2018). ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE. IEEE SigPort. http://sigport.org/3104
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen, 2018. ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE. Available at: http://sigport.org/3104.
Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. (2018). "ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE." Web.
1. Xin Leng, Jingdong Chen, Jacob Benesty, Israel Cohen. ON SPEECH ENHANCEMENT USING MICROPHONE ARRAYS IN THE PRESENCE OF CO-DIRECTIONAL INTERFERENCE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3104

CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS


Coral species, with complex morphology and ambiguous boundaries, pose a great challenge for automated classification. CNN activations, which are extracted from fully connected layers of deep networks (FC features), have been successfully used as powerful universal representations in many visual tasks. In this paper, we investigate the transferability and combined performance of FC features and CONV features (extracted

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Authors:
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid
Submitted On:
20 April 2018 - 8:59am
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ICASSP2018 Poster.pdf

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[1] Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, "CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3103. Accessed: Apr. 21, 2018.
@article{3103-18,
url = {http://sigport.org/3103},
author = {Mohammed Bennamoun; Senjian An; Ferdous Sohel; Farid Boussaid },
publisher = {IEEE SigPort},
title = {CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS},
year = {2018} }
TY - EJOUR
T1 - CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS
AU - Mohammed Bennamoun; Senjian An; Ferdous Sohel; Farid Boussaid
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3103
ER -
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. (2018). CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS. IEEE SigPort. http://sigport.org/3103
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid, 2018. CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS. Available at: http://sigport.org/3103.
Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. (2018). "CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS." Web.
1. Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid. CLASSIFICATION OF CORALS IN REFLECTANCE AND FLUORESCENCE IMAGES USING CONVOLUTIONAL NEURAL NETWORK REPRESENTATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3103

VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning


In this paper, we propose a novel virtual reality image quality assessment (VR IQA) with adversarial learning for omnidirectional images. To take into account the characteristics of the omnidirectional image, we devise deep networks including novel quality score predictor and human perception guider. The proposed quality score predictor automatically predicts the quality score of distorted image using the latent spatial and position feature.

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Authors:
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro
Submitted On:
20 April 2018 - 8:00am
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VR IQA NET-ICASSP2018

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[1] Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro, "VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3102. Accessed: Apr. 21, 2018.
@article{3102-18,
url = {http://sigport.org/3102},
author = {Heoun-taek Lim; Hak Gu Kim; and Yong Man Ro },
publisher = {IEEE SigPort},
title = {VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning},
year = {2018} }
TY - EJOUR
T1 - VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
AU - Heoun-taek Lim; Hak Gu Kim; and Yong Man Ro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3102
ER -
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. (2018). VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning. IEEE SigPort. http://sigport.org/3102
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro, 2018. VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning. Available at: http://sigport.org/3102.
Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. (2018). "VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning." Web.
1. Heoun-taek Lim, Hak Gu Kim, and Yong Man Ro. VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3102

DEEP FACTORIZATION FOR SPEECH SIGNAL


Various informative factors mixed in speech signals, leading to great difficulty when decoding any of the factors. An intuitive idea is to factorize each speech frame into individual informative factors, though it turns out to be highly difficult. Recently, we found that speaker traits, which were assumed to be long-term distributional properties, are actually short-time patterns, and can be learned by a carefully designed deep neural network (DNN). This discovery motivated a cascade deep factorization (CDF) framework that will be presented in this paper.

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Authors:
Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng
Submitted On:
20 April 2018 - 7:42am
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180417-deepFactor-LLT.pptx

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[1] Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng, "DEEP FACTORIZATION FOR SPEECH SIGNAL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3101. Accessed: Apr. 21, 2018.
@article{3101-18,
url = {http://sigport.org/3101},
author = {Lantian Li; Dong Wang; Yixiang Chen; Ying Shi; Zhiyuan Tang; Thomas Fang Zheng },
publisher = {IEEE SigPort},
title = {DEEP FACTORIZATION FOR SPEECH SIGNAL},
year = {2018} }
TY - EJOUR
T1 - DEEP FACTORIZATION FOR SPEECH SIGNAL
AU - Lantian Li; Dong Wang; Yixiang Chen; Ying Shi; Zhiyuan Tang; Thomas Fang Zheng
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3101
ER -
Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng. (2018). DEEP FACTORIZATION FOR SPEECH SIGNAL. IEEE SigPort. http://sigport.org/3101
Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng, 2018. DEEP FACTORIZATION FOR SPEECH SIGNAL. Available at: http://sigport.org/3101.
Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng. (2018). "DEEP FACTORIZATION FOR SPEECH SIGNAL." Web.
1. Lantian Li, Dong Wang, Yixiang Chen, Ying Shi, Zhiyuan Tang, Thomas Fang Zheng. DEEP FACTORIZATION FOR SPEECH SIGNAL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3101

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