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Independent component analysis (MLR-ICAN)

A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION

D J Lee

ICIP_CNN.pdf

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Authors:
D J Lee
Yao Chou, Dah-Jye Lee, Dong Zhang
Submitted On:
14 September 2017 - 5:18pm
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ICIP_CNN.pdf

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[1] Yao Chou, Dah-Jye Lee, Dong Zhang, "A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2048. Accessed: Sep. 20, 2017.
@article{2048-17,
url = {http://sigport.org/2048},
author = {Yao Chou; Dah-Jye Lee; Dong Zhang },
publisher = {IEEE SigPort},
title = {A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION },
year = {2017} }
TY - EJOUR
T1 - A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION
AU - Yao Chou; Dah-Jye Lee; Dong Zhang
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2048
ER -
Yao Chou, Dah-Jye Lee, Dong Zhang. (2017). A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION . IEEE SigPort. http://sigport.org/2048
Yao Chou, Dah-Jye Lee, Dong Zhang, 2017. A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION . Available at: http://sigport.org/2048.
Yao Chou, Dah-Jye Lee, Dong Zhang. (2017). "A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION ." Web.
1. Yao Chou, Dah-Jye Lee, Dong Zhang. A PARALLEL CONVOLUTIONAL NEURAL NETWORK ARCHITECTURE FOR STEREO VISION ESTIMATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2048

A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis

Lei Sun

Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure.

ica_slide.pdf

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Authors:
Lei Sun
Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin
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24 March 2016 - 10:03am
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ica_slide.pdf

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ica_poster1.pptx

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[1] Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin, "A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/624. Accessed: Sep. 20, 2017.
@article{624-16,
url = {http://sigport.org/624},
author = {Lei Sun; Badong Chen; Kar-Ann Toh; Zhiping Lin },
publisher = {IEEE SigPort},
title = {A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis},
year = {2016} }
TY - EJOUR
T1 - A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis
AU - Lei Sun; Badong Chen; Kar-Ann Toh; Zhiping Lin
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/624
ER -
Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin. (2016). A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis. IEEE SigPort. http://sigport.org/624
Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin, 2016. A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis. Available at: http://sigport.org/624.
Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin. (2016). "A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis." Web.
1. Lei Sun, Badong Chen, Kar-Ann Toh, Zhiping Lin. A Parameter-Free Cauchy-Schwartz Information Measure for Independent Component Analysis [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/624

Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA

Muhammad Azam

In this paper, bounded generalized Gaussian mixture model (BGGMM) using independent component analysis (ICA) is proposed and applied to an existing unsupervised keyword spotting setting for the generation of posteriorgrams. The ICA mixture model is trained without any transcription information to generate the posteriorgrams which further labels the speech frames of the keyword example(s) and test data.

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Authors:
Muhammad Azam
Nizar Bouguila
Submitted On:
23 February 2016 - 1:44pm
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Unsupervised keyword Spotting_Slides_GlobalSIP_2015.pdf

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[1] Nizar Bouguila, "Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/448. Accessed: Sep. 20, 2017.
@article{448-15,
url = {http://sigport.org/448},
author = {Nizar Bouguila },
publisher = {IEEE SigPort},
title = {Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA},
year = {2015} }
TY - EJOUR
T1 - Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA
AU - Nizar Bouguila
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/448
ER -
Nizar Bouguila. (2015). Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA. IEEE SigPort. http://sigport.org/448
Nizar Bouguila, 2015. Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA. Available at: http://sigport.org/448.
Nizar Bouguila. (2015). "Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA." Web.
1. Nizar Bouguila. Unsupervised Keyword Spotting using Bounded Generalized Gaussian Mixture Model with ICA [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/448