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Machine Learning for Signal Processing

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.

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Authors:
Anh T. Pham, Raviv Raich, and Xiaoli Z. Fern
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25 April 2018 - 2:00pm
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Discriminative Clustering with Cardinality Constraint_ICASSP2018_latest.pdf

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[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: Oct. 19, 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

A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations

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Authors:
Pascal Bianchi, Walid Hachem
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24 April 2018 - 1:14pm
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stochastic-douglas-rachford.pdf

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[1] Pascal Bianchi, Walid Hachem, "A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3170. Accessed: Oct. 19, 2018.
@article{3170-18,
url = {http://sigport.org/3170},
author = {Pascal Bianchi; Walid Hachem },
publisher = {IEEE SigPort},
title = {A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations},
year = {2018} }
TY - EJOUR
T1 - A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations
AU - Pascal Bianchi; Walid Hachem
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3170
ER -
Pascal Bianchi, Walid Hachem. (2018). A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations. IEEE SigPort. http://sigport.org/3170
Pascal Bianchi, Walid Hachem, 2018. A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations. Available at: http://sigport.org/3170.
Pascal Bianchi, Walid Hachem. (2018). "A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations." Web.
1. Pascal Bianchi, Walid Hachem. A Constant Step Stochastic Douglas Rachford Algorithm with Application to Non Separable Regularizations [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3170

AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO

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Authors:
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu
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23 April 2018 - 8:52pm
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icassp_poster.pdf

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[1] Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu, "AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3155. Accessed: Oct. 19, 2018.
@article{3155-18,
url = {http://sigport.org/3155},
author = {Alistair Letcher; Jelena Trišović; Collin Cademartori; Xi Chen; Jason Xu },
publisher = {IEEE SigPort},
title = {AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO},
year = {2018} }
TY - EJOUR
T1 - AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO
AU - Alistair Letcher; Jelena Trišović; Collin Cademartori; Xi Chen; Jason Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3155
ER -
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. (2018). AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO. IEEE SigPort. http://sigport.org/3155
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu, 2018. AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO. Available at: http://sigport.org/3155.
Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. (2018). "AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO." Web.
1. Alistair Letcher, Jelena Trišović, Collin Cademartori, Xi Chen, Jason Xu. AUTOMATIC CONFLICT DETECTION IN POLICE BODY-WORN AUDIO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3155

Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution


Hyperspectral super-resolution (HSR) is a problem of recovering a high-spectral-spatial-resolution image from a multispectral measurement and a hyperspectral measurement, which have low spectral and spatial resolutions, respectively. We consider a low-rank structured matrix factorization formulation for HSR, which is a non-convex large-scale optimization problem. Our contributions contain both computational and theoretical aspects.

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Authors:
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu
Submitted On:
19 April 2018 - 10:39pm
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ICASSP 2018 modified.pdf

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[1] Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3057. Accessed: Oct. 19, 2018.
@article{3057-18,
url = {http://sigport.org/3057},
author = {Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu },
publisher = {IEEE SigPort},
title = {Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution},
year = {2018} }
TY - EJOUR
T1 - Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution
AU - Ruiyuan Wu; Chun-Hei Chan; Hoi-To Wai; Wing-Kin Ma; and Xiao Fu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3057
ER -
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. IEEE SigPort. http://sigport.org/3057
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu, 2018. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution. Available at: http://sigport.org/3057.
Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. (2018). "Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution." Web.
1. Ruiyuan Wu, Chun-Hei Chan, Hoi-To Wai, Wing-Kin Ma, and Xiao Fu. Hi, BCD! Hybrid Inexact Block Coordinate Descent for Hyperspectral Super-Resolution [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3057

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network


Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean.

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Authors:
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn
Submitted On:
19 April 2018 - 4:44pm
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language-noise-transfer.pdf

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[1] Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3025. Accessed: Oct. 19, 2018.
@article{3025-18,
url = {http://sigport.org/3025},
author = {Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn },
publisher = {IEEE SigPort},
title = {Language and Noise Transfer in Speech Enhancement Generative Adversarial Network},
year = {2018} }
TY - EJOUR
T1 - Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
AU - Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3025
ER -
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. IEEE SigPort. http://sigport.org/3025
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, 2018. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. Available at: http://sigport.org/3025.
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network." Web.
1. Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3025

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network


Speech enhancement deep learning systems usually require large amounts of training data to operate in broad conditions or real applications. This makes the adaptability of those systems into new, low resource environments an important topic. In this work, we present the results of adapting a speech enhancement generative adversarial network by fine-tuning the generator with small amounts of data. We investigate the minimum requirements to obtain a stable behavior in terms of several objective metrics in two very different languages: Catalan and Korean.

Paper Details

Authors:
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn
Submitted On:
19 April 2018 - 4:40pm
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language-noise-transfer.pdf

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[1] Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3023. Accessed: Oct. 19, 2018.
@article{3023-18,
url = {http://sigport.org/3023},
author = {Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn },
publisher = {IEEE SigPort},
title = {Language and Noise Transfer in Speech Enhancement Generative Adversarial Network},
year = {2018} }
TY - EJOUR
T1 - Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
AU - Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3023
ER -
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. IEEE SigPort. http://sigport.org/3023
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, 2018. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. Available at: http://sigport.org/3023.
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network." Web.
1. Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3023

Mobile Bayesian Spectrum Learning for Heterogeneous Networks


Spectrum sensing in heterogeneous networks is very challenging as it usually requires a large number of static secondary users (SUs) to obtain the global spectrum states. In this paper, we tackle the spectrum sensing in heterogeneous networks from a new perspective. We exploit the mobility of multiple SUs to simultaneously collect spatial-temporal spectrum sensing data. Then, we propose a novel non-parametric Bayesian learning model, referred to as beta process hidden Markov model to capture the spatio-temporal correlation in the collected spectrum data.

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Submitted On:
19 April 2018 - 3:01pm
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ICASSP Poster.pdf

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[1] , "Mobile Bayesian Spectrum Learning for Heterogeneous Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3007. Accessed: Oct. 19, 2018.
@article{3007-18,
url = {http://sigport.org/3007},
author = { },
publisher = {IEEE SigPort},
title = {Mobile Bayesian Spectrum Learning for Heterogeneous Networks},
year = {2018} }
TY - EJOUR
T1 - Mobile Bayesian Spectrum Learning for Heterogeneous Networks
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3007
ER -
. (2018). Mobile Bayesian Spectrum Learning for Heterogeneous Networks. IEEE SigPort. http://sigport.org/3007
, 2018. Mobile Bayesian Spectrum Learning for Heterogeneous Networks. Available at: http://sigport.org/3007.
. (2018). "Mobile Bayesian Spectrum Learning for Heterogeneous Networks." Web.
1. . Mobile Bayesian Spectrum Learning for Heterogeneous Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3007

ICASSP2018-SADL

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Submitted On:
19 April 2018 - 2:53pm
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ICASSP2018-SADL.pdf

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[1] , "ICASSP2018-SADL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3005. Accessed: Oct. 19, 2018.
@article{3005-18,
url = {http://sigport.org/3005},
author = { },
publisher = {IEEE SigPort},
title = {ICASSP2018-SADL},
year = {2018} }
TY - EJOUR
T1 - ICASSP2018-SADL
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3005
ER -
. (2018). ICASSP2018-SADL. IEEE SigPort. http://sigport.org/3005
, 2018. ICASSP2018-SADL. Available at: http://sigport.org/3005.
. (2018). "ICASSP2018-SADL." Web.
1. . ICASSP2018-SADL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3005

Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces


Our work is based on a recently introduced mathematical theory of deep convolutional neural networks (DCNNs).
It was shown that DCNNs are stable with respect to deformations of bandlimited input functions.
In the present paper, we generalize this result: We prove deformation stability on Sobolev spaces.
Further, we show a weak form of deformation stability for the whole input space L2.
The basic components of DCNNs are semi-discrete frames.
For practical applications, a concrete choice is necessary.

talk.pdf

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Authors:
Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche
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19 April 2018 - 2:26pm
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[1] Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche, "Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2996. Accessed: Oct. 19, 2018.
@article{2996-18,
url = {http://sigport.org/2996},
author = {Michael Koller; Johannes Großmann; Ullrich Mönich; Holger Boche },
publisher = {IEEE SigPort},
title = {Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces},
year = {2018} }
TY - EJOUR
T1 - Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces
AU - Michael Koller; Johannes Großmann; Ullrich Mönich; Holger Boche
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2996
ER -
Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche. (2018). Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces. IEEE SigPort. http://sigport.org/2996
Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche, 2018. Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces. Available at: http://sigport.org/2996.
Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche. (2018). "Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces." Web.
1. Michael Koller, Johannes Großmann, Ullrich Mönich, Holger Boche. Deformation Stability of Deep Convolutional Neural Networks on Sobolev Spaces [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2996

Language and Noise Transfer in Speech Enhancement Generative Adversarial Network

Paper Details

Authors:
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn
Submitted On:
19 April 2018 - 4:40pm
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language-noise-transfer.pdf

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[1] Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2979. Accessed: Oct. 19, 2018.
@article{2979-18,
url = {http://sigport.org/2979},
author = {Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn },
publisher = {IEEE SigPort},
title = {Language and Noise Transfer in Speech Enhancement Generative Adversarial Network},
year = {2018} }
TY - EJOUR
T1 - Language and Noise Transfer in Speech Enhancement Generative Adversarial Network
AU - Maruchan Park; Joan Serrà; Antonio Bonafonte; Kang-Hun Ahn
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2979
ER -
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. IEEE SigPort. http://sigport.org/2979
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn, 2018. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network. Available at: http://sigport.org/2979.
Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. (2018). "Language and Noise Transfer in Speech Enhancement Generative Adversarial Network." Web.
1. Maruchan Park, Joan Serrà, Antonio Bonafonte, Kang-Hun Ahn. Language and Noise Transfer in Speech Enhancement Generative Adversarial Network [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2979

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