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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.

Deep attractor networks for speaker re-identification and blind source separation


Deep Clustering (DC) and Deep Attractor Networks (DANs) are a data-driven way to monaural blind source separation.
Both approaches provide astonishing single channel performance but have not yet been generalized to block-online processing.
When separating speech in a continuous stream with a block-online algorithm, it needs to be determined in each block which of the output streams belongs to whom.
In this contribution we solve this block permutation problem by introducing an additional speaker identification embedding to the DAN model structure.

Paper Details

Authors:
Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach
Submitted On:
19 April 2018 - 7:00pm
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2018-04-17_drude.pdf

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[1] Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach, "Deep attractor networks for speaker re-identification and blind source separation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3037. Accessed: Apr. 21, 2018.
@article{3037-18,
url = {http://sigport.org/3037},
author = {Lukas Drude; Thilo von Neumann; Reinhold Haeb-Umbach },
publisher = {IEEE SigPort},
title = {Deep attractor networks for speaker re-identification and blind source separation},
year = {2018} }
TY - EJOUR
T1 - Deep attractor networks for speaker re-identification and blind source separation
AU - Lukas Drude; Thilo von Neumann; Reinhold Haeb-Umbach
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3037
ER -
Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach. (2018). Deep attractor networks for speaker re-identification and blind source separation. IEEE SigPort. http://sigport.org/3037
Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach, 2018. Deep attractor networks for speaker re-identification and blind source separation. Available at: http://sigport.org/3037.
Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach. (2018). "Deep attractor networks for speaker re-identification and blind source separation." Web.
1. Lukas Drude, Thilo von Neumann, Reinhold Haeb-Umbach. Deep attractor networks for speaker re-identification and blind source separation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3037

AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY


This paper presents an approach based on acoustic analysis to describe gender equality in French audiovisual streams, through the estimation of male and female speaking time. Gender detection systems based on Gaussian Mixture Models, i-vectors and Convolutional Neural Networks (CNN) were trained using an internal database of 2,284 French speakers and evaluated using REPERE challenge corpus. The CNN system obtained the best performance with a frame-level gender detection F-measure of 96.52 and a hourly women speaking time percentage error bellow 0.6%.

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Authors:
Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier
Submitted On:
19 April 2018 - 6:49pm
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icasspPoster.pdf

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[1] Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier, "AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3036. Accessed: Apr. 21, 2018.
@article{3036-18,
url = {http://sigport.org/3036},
author = {Jean Carrive; Félicien Vallet; Anthony Larcher; Sylvain Meignier },
publisher = {IEEE SigPort},
title = {AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY},
year = {2018} }
TY - EJOUR
T1 - AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY
AU - Jean Carrive; Félicien Vallet; Anthony Larcher; Sylvain Meignier
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3036
ER -
Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier. (2018). AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY. IEEE SigPort. http://sigport.org/3036
Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier, 2018. AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY. Available at: http://sigport.org/3036.
Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier. (2018). "AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY." Web.
1. Jean Carrive, Félicien Vallet, Anthony Larcher, Sylvain Meignier. AN OPEN-SOURCE SPEAKER GENDER DETECTION FRAMEWORK FOR MONITORING GENDER EQUALITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3036

Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models

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Authors:
Longxin Li, Man-Wai Mak
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19 April 2018 - 5:51pm
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2018icassp_latest.pdf

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[1] Longxin Li, Man-Wai Mak, "Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3034. Accessed: Apr. 21, 2018.
@article{3034-18,
url = {http://sigport.org/3034},
author = {Longxin Li; Man-Wai Mak },
publisher = {IEEE SigPort},
title = {Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models
AU - Longxin Li; Man-Wai Mak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3034
ER -
Longxin Li, Man-Wai Mak. (2018). Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models. IEEE SigPort. http://sigport.org/3034
Longxin Li, Man-Wai Mak, 2018. Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models. Available at: http://sigport.org/3034.
Longxin Li, Man-Wai Mak. (2018). "Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models." Web.
1. Longxin Li, Man-Wai Mak. Unsupervised Domain Adaptation for Gender-Aware PLDA Mixture Models [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3034

Twitter User Geolocation using Multivew Deep Learning


Predicting the geographical location of users on social networks like Twitter is an active research topic with plenty of methods proposed so far. Most of the existing work follows either a content-based or a network-based approach. The former is based on user-generated content while the latter exploits the structure of the network of users. In this paper, we propose a more generic approach, which incorporates not only both content-based and network-based features, but also other available information into a unified model.

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20 April 2018 - 4:22am
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icassp_2018_twitter.pdf

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icassp_2018_twitter.pdf

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[1] , "Twitter User Geolocation using Multivew Deep Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3033. Accessed: Apr. 21, 2018.
@article{3033-18,
url = {http://sigport.org/3033},
author = { },
publisher = {IEEE SigPort},
title = {Twitter User Geolocation using Multivew Deep Learning},
year = {2018} }
TY - EJOUR
T1 - Twitter User Geolocation using Multivew Deep Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3033
ER -
. (2018). Twitter User Geolocation using Multivew Deep Learning. IEEE SigPort. http://sigport.org/3033
, 2018. Twitter User Geolocation using Multivew Deep Learning. Available at: http://sigport.org/3033.
. (2018). "Twitter User Geolocation using Multivew Deep Learning." Web.
1. . Twitter User Geolocation using Multivew Deep Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3033

MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS


We present a source separation system for high-order ambisonics (HOA) contents. We derive a multichannel spatial filter from a mask estimated by a long short-term memory (LSTM) recurrent neural network. We combine one channel of the mixture with the outputs of basic HOA beamformers as inputs to the LSTM, assuming that we know the directions of arrival of the directional sources. In our experiments, the speech of interest can be corrupted either by diffuse noise or by an equally loud competing speaker.

perotin.pdf

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Authors:
Emmanuel Vincent, Alexandre Guérin
Submitted On:
19 April 2018 - 5:18pm
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perotin.pdf

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[1] Emmanuel Vincent, Alexandre Guérin, "MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3031. Accessed: Apr. 21, 2018.
@article{3031-18,
url = {http://sigport.org/3031},
author = {Emmanuel Vincent; Alexandre Guérin },
publisher = {IEEE SigPort},
title = {MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS},
year = {2018} }
TY - EJOUR
T1 - MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS
AU - Emmanuel Vincent; Alexandre Guérin
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3031
ER -
Emmanuel Vincent, Alexandre Guérin. (2018). MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS. IEEE SigPort. http://sigport.org/3031
Emmanuel Vincent, Alexandre Guérin, 2018. MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS. Available at: http://sigport.org/3031.
Emmanuel Vincent, Alexandre Guérin. (2018). "MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS." Web.
1. Emmanuel Vincent, Alexandre Guérin. MULTICHANNEL SPEECH SEPARATION WITH RECURRENT NEURAL NETWORKS FROM HIGH-ORDER AMBISONICS RECORDINGS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3031

DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS


In the context of wireless communications, we propose a deep learning approach to learn the mapping from the instantaneous state of a frequency selective fading channel to the corresponding frame error probability (FEP) for an arbitrary set of transmission parameters. We propose an abstract model of a bit interleaved coded modulation (BICM) orthogonal frequency division multiplexing (OFDM) link chain and show that the maximum likelihood (ML) estimator of the model parameters estimates the true FEP distribution.

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Authors:
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson
Submitted On:
19 April 2018 - 5:17pm
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Deep Learning for FEP Prediction.pdf

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[1] Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, "DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3030. Accessed: Apr. 21, 2018.
@article{3030-18,
url = {http://sigport.org/3030},
author = {Vidit Saxena; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson },
publisher = {IEEE SigPort},
title = {DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS},
year = {2018} }
TY - EJOUR
T1 - DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS
AU - Vidit Saxena; Joakim Jaldén; Hugo Tullberg; Mats Bengtsson
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3030
ER -
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS. IEEE SigPort. http://sigport.org/3030
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson, 2018. DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS. Available at: http://sigport.org/3030.
Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. (2018). "DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS." Web.
1. Vidit Saxena, Joakim Jaldén, Hugo Tullberg, Mats Bengtsson. DEEP LEARNING FOR FRAME ERROR PROBABILITY PREDICTION IN BICM-OFDM SYSTEMS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3030

PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK


In this paper, we study the beam-based training design for the single-user (SU) hybrid massive antenna system based on outage probability performance. First, an interleaved training design is proposed where the feedback is concatenated with the training procedure to monitor the training status and to have the training length adaptive to the channel realization. Then, the average training length and outage probability are derived for the proposed interleaved training and SU transmission.

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19 April 2018 - 5:13pm
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PPT For the presentation of paper "PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK"

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[1] , "PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3029. Accessed: Apr. 21, 2018.
@article{3029-18,
url = {http://sigport.org/3029},
author = { },
publisher = {IEEE SigPort},
title = {PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK},
year = {2018} }
TY - EJOUR
T1 - PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3029
ER -
. (2018). PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK. IEEE SigPort. http://sigport.org/3029
, 2018. PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK. Available at: http://sigport.org/3029.
. (2018). "PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK." Web.
1. . PERFORMANCE OF INTERLEAVED TRAINING FOR SINGLE-USER HYBRID MASSIVE ANTENNA DOWNLINK [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3029

Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation


In this paper, we present one saliency-based feature selection and tracking strategy in the feature-based stereoscopic panoramic video generation system. Many existing stereoscopic video composition approaches aims at producing high-quality panoramas from multiple input cameras; however, most of them directly operate image alignment on those originally detected features without any refinement or optimization. The standard global feature extraction threshold always fails to guarantee stitching correctness of all human interested regions.

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Authors:
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld
Submitted On:
19 April 2018 - 5:06pm
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ICASSP_2018_20180416.pdf

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[1] Haoyu Wang, Daniel J. Sandin, Dan Schonfeld, "Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3028. Accessed: Apr. 21, 2018.
@article{3028-18,
url = {http://sigport.org/3028},
author = {Haoyu Wang; Daniel J. Sandin; Dan Schonfeld },
publisher = {IEEE SigPort},
title = {Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation},
year = {2018} }
TY - EJOUR
T1 - Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation
AU - Haoyu Wang; Daniel J. Sandin; Dan Schonfeld
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3028
ER -
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. (2018). Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation. IEEE SigPort. http://sigport.org/3028
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld, 2018. Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation. Available at: http://sigport.org/3028.
Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. (2018). "Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation." Web.
1. Haoyu Wang, Daniel J. Sandin, Dan Schonfeld. Saliency-based Feature Selection Strategy in Stereoscopic Panoramic Video Generation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3028

Demixing and blind deconvolution of graph-diffused signals


We extend the classical joint problem of signal demixing, blind deconvolution,
and filter identification to the realm of graphs. The model is that
each mixing signal is generated by a sparse input diffused via a graph filter.
Then, the sum of diffused signals is observed. We identify and address
two problems: 1) each sparse input is diffused in a different graph; and 2)
all signals are diffused in the same graph. These tasks amount to finding
the collections of sources and filter coefficients producing the observation.

Paper Details

Authors:
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez
Submitted On:
19 April 2018 - 4:51pm
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ICASSP2018_demixing_GSP_poster_v2.pdf

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[1] Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, "Demixing and blind deconvolution of graph-diffused signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3027. Accessed: Apr. 21, 2018.
@article{3027-18,
url = {http://sigport.org/3027},
author = {Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez },
publisher = {IEEE SigPort},
title = {Demixing and blind deconvolution of graph-diffused signals},
year = {2018} }
TY - EJOUR
T1 - Demixing and blind deconvolution of graph-diffused signals
AU - Fernando J. Iglesias; Santiago Segarra; Samuel Rey-Escudero; Antonio G. Marques; David Ramirez
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3027
ER -
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). Demixing and blind deconvolution of graph-diffused signals. IEEE SigPort. http://sigport.org/3027
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez, 2018. Demixing and blind deconvolution of graph-diffused signals. Available at: http://sigport.org/3027.
Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. (2018). "Demixing and blind deconvolution of graph-diffused signals." Web.
1. Fernando J. Iglesias, Santiago Segarra, Samuel Rey-Escudero, Antonio G. Marques, David Ramirez. Demixing and blind deconvolution of graph-diffused signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3027

An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings

Paper Details

Authors:
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT
Submitted On:
19 April 2018 - 4:47pm
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ICASSP2018.pptx

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[1] Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT, "An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3026. Accessed: Apr. 21, 2018.
@article{3026-18,
url = {http://sigport.org/3026},
author = {Jean-Baptiste SCHIRATTI; Jean-Eudes LE DOUGET; Michel LE VAN QUYEN; Slim ESSID; Alexandre GRAMFORT },
publisher = {IEEE SigPort},
title = {An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings},
year = {2018} }
TY - EJOUR
T1 - An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings
AU - Jean-Baptiste SCHIRATTI; Jean-Eudes LE DOUGET; Michel LE VAN QUYEN; Slim ESSID; Alexandre GRAMFORT
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3026
ER -
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. (2018). An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings. IEEE SigPort. http://sigport.org/3026
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT, 2018. An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings. Available at: http://sigport.org/3026.
Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. (2018). "An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings." Web.
1. Jean-Baptiste SCHIRATTI, Jean-Eudes LE DOUGET, Michel LE VAN QUYEN, Slim ESSID, Alexandre GRAMFORT. An Ensemble Learning Approach To Detect Epileptic Seizures From Long Intracranial EEG Recordings [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3026

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