Sorry, you need to enable JavaScript to visit this website.

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.

Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals


In this paper, we present completely automated cardiac anomaly detection for remote screening of cardio-vascular abnormality using Phonocardiogram (PCG) or heart sound signal. Even though PCG contains significant and vital cardiac health information and cardiac abnormality signature, the presence of substantial noise does not guarantee highly effective analysis of cardiac condition. Our proposed method intelligently identifies and eliminates noisy PCG signal and consequently detects pathological abnormality condition. We further present a unified model of hybrid feature selection method.

Paper Details

Authors:
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal
Submitted On:
27 April 2018 - 2:44am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP_Paper_2030_final.pdf

(81 downloads)

Subscribe

[1] Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3184. Accessed: Nov. 13, 2018.
@article{3184-18,
url = {http://sigport.org/3184},
author = {Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal },
publisher = {IEEE SigPort},
title = {Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals},
year = {2018} }
TY - EJOUR
T1 - Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals
AU - Arijit Ukil; Soma Bnadyopadhyay; Chetanya Puri; Rituraj Singh; Arpan Pal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3184
ER -
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. IEEE SigPort. http://sigport.org/3184
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal, 2018. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals. Available at: http://sigport.org/3184.
Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. (2018). "Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals." Web.
1. Arijit Ukil, Soma Bnadyopadhyay, Chetanya Puri, Rituraj Singh, Arpan Pal. Effective Noise Removal and Unified Model of Hybrid Feature Space Optimization for Automated Cardiac Anomaly Detection using phonocardiogarm signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3184

ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM


This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems.
Firstly, a lossy compression of the past hidden layer outputs (history vector) with caching is introduced in order to reduce the number of LM queries.
Next, RNNLM computations are deployed in a CPU-GPU hybrid manner, which computes each layer of the model on a more advantageous platform.
The added overhead by data exchanges between CPU and GPU is compensated through a frame-wise batching strategy.

Paper Details

Authors:
Chiyoun Park, Namhoon Kim, Jaewon Lee
Submitted On:
26 April 2018 - 1:10am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Icassp2018_KML_20180402_poster.pdf

(100 downloads)

Subscribe

[1] Chiyoun Park, Namhoon Kim, Jaewon Lee, "ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3183. Accessed: Nov. 13, 2018.
@article{3183-18,
url = {http://sigport.org/3183},
author = {Chiyoun Park; Namhoon Kim; Jaewon Lee },
publisher = {IEEE SigPort},
title = {ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM},
year = {2018} }
TY - EJOUR
T1 - ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM
AU - Chiyoun Park; Namhoon Kim; Jaewon Lee
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3183
ER -
Chiyoun Park, Namhoon Kim, Jaewon Lee. (2018). ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM. IEEE SigPort. http://sigport.org/3183
Chiyoun Park, Namhoon Kim, Jaewon Lee, 2018. ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM. Available at: http://sigport.org/3183.
Chiyoun Park, Namhoon Kim, Jaewon Lee. (2018). "ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM." Web.
1. Chiyoun Park, Namhoon Kim, Jaewon Lee. ACCELERATING RECURRENT NEURAL NETWORK LANGUAGE MODEL BASED ONLINE SPEECH RECOGNITION SYSTEM [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3183

On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption


In this paper, we compare the performance of two active dereverberation techniques using a planar array of microphones and loudspeakers. The two techniques are based on a solution to the Kirchhoff-Helmholtz Integral Equation (KHIE). We adapt a Wave Field Synthesis (WFS) based method to the application of real-time 3D dereverberation by using a low-latency pre-filter design. The use of First-Order Differential (FOD) models is also proposed as an alternative method to the use of monopoles with WFS and which does not assume knowledge of the room geometry or primary sources.

Paper Details

Authors:
Christian Ritz, W. Bastiaan Kleijn
Submitted On:
26 April 2018 - 12:55am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster presentation

(187 downloads)

Subscribe

[1] Christian Ritz, W. Bastiaan Kleijn, "On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3182. Accessed: Nov. 13, 2018.
@article{3182-18,
url = {http://sigport.org/3182},
author = {Christian Ritz; W. Bastiaan Kleijn },
publisher = {IEEE SigPort},
title = {On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption},
year = {2018} }
TY - EJOUR
T1 - On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption
AU - Christian Ritz; W. Bastiaan Kleijn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3182
ER -
Christian Ritz, W. Bastiaan Kleijn. (2018). On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption. IEEE SigPort. http://sigport.org/3182
Christian Ritz, W. Bastiaan Kleijn, 2018. On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption. Available at: http://sigport.org/3182.
Christian Ritz, W. Bastiaan Kleijn. (2018). "On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption." Web.
1. Christian Ritz, W. Bastiaan Kleijn. On the Comparison of Two Room Compensation / Dereverberation Methods Employing Active Acoustic Boundary Absorption [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3182

Discriminative Clustering with Cardinality Constraints


Clustering is widely used for exploratory data analysis in a variety of applications. Traditionally clustering is studied as an unsupervised task where no human inputs are provided. A recent trend in clustering is to leverage user provided side information to better infer the clustering structure in data. In this paper, we propose a probabilistic graphical model that allows user to provide as input the desired cluster sizes, namely the cardinality constraints. Our model also incorporates a flexible mechanism to inject control of the crispness of the clusters.

Paper Details

Authors:
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern
Submitted On:
25 April 2018 - 2:00pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Discriminative Clustering with Cardinality Constraint_ICASSP2018_latest.pdf

(99 downloads)

Subscribe

[1] Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, "Discriminative Clustering with Cardinality Constraints", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3181. Accessed: Nov. 13, 2018.
@article{3181-18,
url = {http://sigport.org/3181},
author = {Anh T. Pham; Raviv Raich; and Xiaoli Z. Fern },
publisher = {IEEE SigPort},
title = {Discriminative Clustering with Cardinality Constraints},
year = {2018} }
TY - EJOUR
T1 - Discriminative Clustering with Cardinality Constraints
AU - Anh T. Pham; Raviv Raich; and Xiaoli Z. Fern
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3181
ER -
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. (2018). Discriminative Clustering with Cardinality Constraints. IEEE SigPort. http://sigport.org/3181
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern, 2018. Discriminative Clustering with Cardinality Constraints. Available at: http://sigport.org/3181.
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. (2018). "Discriminative Clustering with Cardinality Constraints." Web.
1. Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern. Discriminative Clustering with Cardinality Constraints [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3181

JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH


We consider the joint estimation of time-varying linear prediction (TVLP) filter coefficients and the excitation signal parameters for the analysis of long-term speech segments. Traditional approaches to TVLP estimation assume linear expansion of the coefficients in a set of known basis functions only. But, excitation signal is also time-varying, which affects the estimation of TVLP filter parameters. In this paper, we propose a Bayesian approach, to incorporate the nature of excitation signal and also adapt regularization of the filter parameters.

poster.pdf

PDF icon poster.pdf (110 downloads)

Paper Details

Authors:
Submitted On:
25 April 2018 - 9:22am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

poster.pdf

(110 downloads)

Subscribe

[1] , "JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3180. Accessed: Nov. 13, 2018.
@article{3180-18,
url = {http://sigport.org/3180},
author = { },
publisher = {IEEE SigPort},
title = {JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH},
year = {2018} }
TY - EJOUR
T1 - JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3180
ER -
. (2018). JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH. IEEE SigPort. http://sigport.org/3180
, 2018. JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH. Available at: http://sigport.org/3180.
. (2018). "JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH." Web.
1. . JOINT BAYESIAN ESTIMATION OF TIME-VARYING LP PARAMETERS AND EXCITATION FOR SPEECH [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3180

Multiple-input neural network-based residual echo suppression


A residual echo suppressor (RES) aims to suppress the residual echo in the output of an acoustic echo canceler (AEC). Spectral-based RES approaches typically estimate the magnitude spectra of the near-end speech and the residual echo from a single input, that is either the far-end speech or the echo computed by the AEC, and derive the RES filter coefficients accordingly. These single inputs do not always suffice to discriminate the near-end speech from the remaining echo.

Paper Details

Authors:
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert
Submitted On:
25 April 2018 - 5:13am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

posterICASSP_CARBAJAL.pdf

(88 downloads)

Subscribe

[1] Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert, "Multiple-input neural network-based residual echo suppression", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3178. Accessed: Nov. 13, 2018.
@article{3178-18,
url = {http://sigport.org/3178},
author = {Guillaume Carbajal; Romain Serizel; Emmanuel Vincent; Eric Humbert },
publisher = {IEEE SigPort},
title = {Multiple-input neural network-based residual echo suppression},
year = {2018} }
TY - EJOUR
T1 - Multiple-input neural network-based residual echo suppression
AU - Guillaume Carbajal; Romain Serizel; Emmanuel Vincent; Eric Humbert
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3178
ER -
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. (2018). Multiple-input neural network-based residual echo suppression. IEEE SigPort. http://sigport.org/3178
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert, 2018. Multiple-input neural network-based residual echo suppression. Available at: http://sigport.org/3178.
Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. (2018). "Multiple-input neural network-based residual echo suppression." Web.
1. Guillaume Carbajal, Romain Serizel, Emmanuel Vincent, Eric Humbert. Multiple-input neural network-based residual echo suppression [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3178

Deep learning for predicting image memorability


Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) - based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem.

Paper Details

Authors:
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty
Submitted On:
25 April 2018 - 4:30am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Presentation_final.pdf

(312 downloads)

Subscribe

[1] Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty, "Deep learning for predicting image memorability", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3176. Accessed: Nov. 13, 2018.
@article{3176-18,
url = {http://sigport.org/3176},
author = {Hammad Squalli-Houssaini; Ngoc Q. K. Duong; Marquant Gwenaelle and Claire-Helene Demarty },
publisher = {IEEE SigPort},
title = {Deep learning for predicting image memorability},
year = {2018} }
TY - EJOUR
T1 - Deep learning for predicting image memorability
AU - Hammad Squalli-Houssaini; Ngoc Q. K. Duong; Marquant Gwenaelle and Claire-Helene Demarty
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3176
ER -
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. (2018). Deep learning for predicting image memorability. IEEE SigPort. http://sigport.org/3176
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty, 2018. Deep learning for predicting image memorability. Available at: http://sigport.org/3176.
Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. (2018). "Deep learning for predicting image memorability." Web.
1. Hammad Squalli-Houssaini, Ngoc Q. K. Duong, Marquant Gwenaelle and Claire-Helene Demarty. Deep learning for predicting image memorability [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3176

CASCADE: Channel-Aware Structured Cosparse Audio DEclipper

Paper Details

Authors:
Clément Gaultier, Nancy Bertin, Rémi Gribonval
Submitted On:
25 April 2018 - 4:14am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

CASCADE

(76 downloads)

Subscribe

[1] Clément Gaultier, Nancy Bertin, Rémi Gribonval, "CASCADE: Channel-Aware Structured Cosparse Audio DEclipper", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3175. Accessed: Nov. 13, 2018.
@article{3175-18,
url = {http://sigport.org/3175},
author = {Clément Gaultier; Nancy Bertin; Rémi Gribonval },
publisher = {IEEE SigPort},
title = {CASCADE: Channel-Aware Structured Cosparse Audio DEclipper},
year = {2018} }
TY - EJOUR
T1 - CASCADE: Channel-Aware Structured Cosparse Audio DEclipper
AU - Clément Gaultier; Nancy Bertin; Rémi Gribonval
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3175
ER -
Clément Gaultier, Nancy Bertin, Rémi Gribonval. (2018). CASCADE: Channel-Aware Structured Cosparse Audio DEclipper. IEEE SigPort. http://sigport.org/3175
Clément Gaultier, Nancy Bertin, Rémi Gribonval, 2018. CASCADE: Channel-Aware Structured Cosparse Audio DEclipper. Available at: http://sigport.org/3175.
Clément Gaultier, Nancy Bertin, Rémi Gribonval. (2018). "CASCADE: Channel-Aware Structured Cosparse Audio DEclipper." Web.
1. Clément Gaultier, Nancy Bertin, Rémi Gribonval. CASCADE: Channel-Aware Structured Cosparse Audio DEclipper [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3175

Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot


We explore new aspects of assistive living on smart human-robot interaction (HRI) that involve automatic recognition and online validation of speech and gestures in a natural interface, providing social features for HRI. We introduce a whole framework and resources of a real-life scenario for elderly subjects supported by an assistive bathing robot, addressing health and hygiene care issues.

Paper Details

Authors:
A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos
Submitted On:
25 April 2018 - 3:48am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Zlatintsi+_HRIforAssistiveBathRobot_ICASSP2018_poster_Final.pdf

(109 downloads)

Subscribe

[1] A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos, "Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3174. Accessed: Nov. 13, 2018.
@article{3174-18,
url = {http://sigport.org/3174},
author = {A. Zlatintsi; I. Rodomagoulakis; P. Koutras; A. C. Dometios; V. Pitsikalis; C. S. Tzafestas; and P. Maragos },
publisher = {IEEE SigPort},
title = {Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot},
year = {2018} }
TY - EJOUR
T1 - Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot
AU - A. Zlatintsi; I. Rodomagoulakis; P. Koutras; A. C. Dometios; V. Pitsikalis; C. S. Tzafestas; and P. Maragos
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3174
ER -
A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos. (2018). Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot. IEEE SigPort. http://sigport.org/3174
A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos, 2018. Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot. Available at: http://sigport.org/3174.
A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos. (2018). "Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot." Web.
1. A. Zlatintsi, I. Rodomagoulakis, P. Koutras, A. C. Dometios, V. Pitsikalis, C. S. Tzafestas, and P. Maragos. Multimodal Signal Processing and Learning Aspects of Human-Robot Interaction for an Assistive Bathing Robot [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3174

Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition


The i-vector approach to speaker recognition has achieved good performance when the domain of the evaluation dataset is similar to that of the training dataset. However, in real-world applications, there is always a mismatch between the training and evaluation datasets, that leads to performance degradation. To address this problem, this paper proposes to learn the domain-invariant and speaker-discriminative speech representations via domain adversarial training.

Paper Details

Authors:
Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li
Submitted On:
25 April 2018 - 2:23am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

icassp2018_slides_qingwang_Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition.pdf

(141 downloads)

Subscribe

[1] Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li, "Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3173. Accessed: Nov. 13, 2018.
@article{3173-18,
url = {http://sigport.org/3173},
author = {Qing Wang; Wei Rao; Sining Sun; Lei Xie; Eng Siong Chng; Haizhou Li },
publisher = {IEEE SigPort},
title = {Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition
AU - Qing Wang; Wei Rao; Sining Sun; Lei Xie; Eng Siong Chng; Haizhou Li
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3173
ER -
Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li. (2018). Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition. IEEE SigPort. http://sigport.org/3173
Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li, 2018. Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition. Available at: http://sigport.org/3173.
Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li. (2018). "Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition." Web.
1. Qing Wang, Wei Rao, Sining Sun, Lei Xie, Eng Siong Chng, Haizhou Li. Unsupervised Domain Adaptation via Domain Adversarial Training for Speaker Recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3173

Pages