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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website

Statistical rank selection for incomplete low-rank matrices

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Authors:
Rui Zhang, Alexander Shapiro, Yao Xie
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15 May 2019 - 7:09pm
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[1] Rui Zhang, Alexander Shapiro, Yao Xie, "Statistical rank selection for incomplete low-rank matrices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4534. Accessed: May. 23, 2019.
@article{4534-19,
url = {http://sigport.org/4534},
author = {Rui Zhang; Alexander Shapiro; Yao Xie },
publisher = {IEEE SigPort},
title = {Statistical rank selection for incomplete low-rank matrices},
year = {2019} }
TY - EJOUR
T1 - Statistical rank selection for incomplete low-rank matrices
AU - Rui Zhang; Alexander Shapiro; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4534
ER -
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). Statistical rank selection for incomplete low-rank matrices. IEEE SigPort. http://sigport.org/4534
Rui Zhang, Alexander Shapiro, Yao Xie, 2019. Statistical rank selection for incomplete low-rank matrices. Available at: http://sigport.org/4534.
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). "Statistical rank selection for incomplete low-rank matrices." Web.
1. Rui Zhang, Alexander Shapiro, Yao Xie. Statistical rank selection for incomplete low-rank matrices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4534

Statistical rank selection for incomplete low-rank matrices

Paper Details

Authors:
Rui Zhang, Alexander Shapiro, Yao Xie
Submitted On:
15 May 2019 - 7:09pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:

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ICASSP2019.pdf

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[1] Rui Zhang, Alexander Shapiro, Yao Xie, "Statistical rank selection for incomplete low-rank matrices", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4533. Accessed: May. 23, 2019.
@article{4533-19,
url = {http://sigport.org/4533},
author = {Rui Zhang; Alexander Shapiro; Yao Xie },
publisher = {IEEE SigPort},
title = {Statistical rank selection for incomplete low-rank matrices},
year = {2019} }
TY - EJOUR
T1 - Statistical rank selection for incomplete low-rank matrices
AU - Rui Zhang; Alexander Shapiro; Yao Xie
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4533
ER -
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). Statistical rank selection for incomplete low-rank matrices. IEEE SigPort. http://sigport.org/4533
Rui Zhang, Alexander Shapiro, Yao Xie, 2019. Statistical rank selection for incomplete low-rank matrices. Available at: http://sigport.org/4533.
Rui Zhang, Alexander Shapiro, Yao Xie. (2019). "Statistical rank selection for incomplete low-rank matrices." Web.
1. Rui Zhang, Alexander Shapiro, Yao Xie. Statistical rank selection for incomplete low-rank matrices [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4533

PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS


In this paper, we analyze the asymptotic performance of a convex optimization-based discrete-valued vector reconstruction from linear measurements. We firstly propose a box-constrained version of the conventional sum of absolute values (SOAV) optimization, which uses a weighted sum of L1 regularizers as a regularizer for the discrete-valued vector. We then derive the asymptotic symbol error rate (SER) performance of the box-constrained SOAV (Box-SOAV) optimization theoretically by using convex Gaussian min-max theorem.

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Authors:
Ryo Hayakawa, Kazunori Hayashi
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15 May 2019 - 5:52pm
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[1] Ryo Hayakawa, Kazunori Hayashi, "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4532. Accessed: May. 23, 2019.
@article{4532-19,
url = {http://sigport.org/4532},
author = {Ryo Hayakawa; Kazunori Hayashi },
publisher = {IEEE SigPort},
title = {PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS},
year = {2019} }
TY - EJOUR
T1 - PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS
AU - Ryo Hayakawa; Kazunori Hayashi
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4532
ER -
Ryo Hayakawa, Kazunori Hayashi. (2019). PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. IEEE SigPort. http://sigport.org/4532
Ryo Hayakawa, Kazunori Hayashi, 2019. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS. Available at: http://sigport.org/4532.
Ryo Hayakawa, Kazunori Hayashi. (2019). "PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS." Web.
1. Ryo Hayakawa, Kazunori Hayashi. PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4532

Sample Space-Time Covariance Estimation


Estimation errors are incurred when calculating the sample space-time covariance matrix. We formulate the variance of this estimator when operating on a finite sample set, compare it to known results, and demonstrate its precision in simulations. The variance of the estimation links directly to previously explored perturbation of the analytic eigenvalues and eigenspaces of a parahermitian cross-spectral density matrix when estimated from finite data.

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Authors:
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss
Submitted On:
15 May 2019 - 4:53pm
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[1] Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss, "Sample Space-Time Covariance Estimation", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4531. Accessed: May. 23, 2019.
@article{4531-19,
url = {http://sigport.org/4531},
author = {Connor Delaosa; Jennifer Pestana; Nicholas J. Goddard; Sam Somasundaram; Stephan Weiss },
publisher = {IEEE SigPort},
title = {Sample Space-Time Covariance Estimation},
year = {2019} }
TY - EJOUR
T1 - Sample Space-Time Covariance Estimation
AU - Connor Delaosa; Jennifer Pestana; Nicholas J. Goddard; Sam Somasundaram; Stephan Weiss
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4531
ER -
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. (2019). Sample Space-Time Covariance Estimation. IEEE SigPort. http://sigport.org/4531
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss, 2019. Sample Space-Time Covariance Estimation. Available at: http://sigport.org/4531.
Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. (2019). "Sample Space-Time Covariance Estimation." Web.
1. Connor Delaosa, Jennifer Pestana, Nicholas J. Goddard, Sam Somasundaram, Stephan Weiss. Sample Space-Time Covariance Estimation [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4531

Aggregation Graph Neural Networks


Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges with neighbors exhibits a regular structure. Thus, regular convolution and regular pooling yield an appropriately regularized GNN.

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Authors:
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro
Submitted On:
15 May 2019 - 2:34pm
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aggregationICASSP19slides.pdf

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[1] Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro, "Aggregation Graph Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4528. Accessed: May. 23, 2019.
@article{4528-19,
url = {http://sigport.org/4528},
author = {Fernando Gama; Antonio G. Marques; Geert Leus; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Aggregation Graph Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Aggregation Graph Neural Networks
AU - Fernando Gama; Antonio G. Marques; Geert Leus; Alejandro Ribeiro
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4528
ER -
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. (2019). Aggregation Graph Neural Networks. IEEE SigPort. http://sigport.org/4528
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro, 2019. Aggregation Graph Neural Networks. Available at: http://sigport.org/4528.
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. (2019). "Aggregation Graph Neural Networks." Web.
1. Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro. Aggregation Graph Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4528

MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION


Current state-of-the-art music boundary detection methods use local features for boundary detection, but such an approach fails to explicitly incorporate the statistical properties of the detected segments. This paper presents a music boundary detection method that simultaneously considers a fitness measure based on the boundary posterior probability, the likelihood of the segmentation duration sequence, and the acoustic consistency within a segment.

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Authors:
Akira Maezawa
Submitted On:
15 May 2019 - 11:50am
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[1] Akira Maezawa, "MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4527. Accessed: May. 23, 2019.
@article{4527-19,
url = {http://sigport.org/4527},
author = {Akira Maezawa },
publisher = {IEEE SigPort},
title = {MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION},
year = {2019} }
TY - EJOUR
T1 - MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION
AU - Akira Maezawa
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4527
ER -
Akira Maezawa. (2019). MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION. IEEE SigPort. http://sigport.org/4527
Akira Maezawa, 2019. MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION. Available at: http://sigport.org/4527.
Akira Maezawa. (2019). "MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION." Web.
1. Akira Maezawa. MUSIC BOUNDARY DETECTION BASED ON A HYBRID DEEP MODEL OF NOVELTY, HOMOGENEITY, REPETITION AND DURATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4527

NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS

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Authors:
Ross Cutler, Ivan Tashev, Johannes Gehrke
Submitted On:
15 May 2019 - 10:26am
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[1] Ross Cutler, Ivan Tashev, Johannes Gehrke, "NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4526. Accessed: May. 23, 2019.
@article{4526-19,
url = {http://sigport.org/4526},
author = {Ross Cutler; Ivan Tashev; Johannes Gehrke },
publisher = {IEEE SigPort},
title = {NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS
AU - Ross Cutler; Ivan Tashev; Johannes Gehrke
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4526
ER -
Ross Cutler, Ivan Tashev, Johannes Gehrke. (2019). NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4526
Ross Cutler, Ivan Tashev, Johannes Gehrke, 2019. NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS. Available at: http://sigport.org/4526.
Ross Cutler, Ivan Tashev, Johannes Gehrke. (2019). "NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS." Web.
1. Ross Cutler, Ivan Tashev, Johannes Gehrke. NON-INTRUSIVE SPEECH QUALITY ASSESSMENT USING NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4526

The Effect of Light Source on ENF Based Video Forensics


ENF (Electric Network Frequency) oscillates around a nominal value (50/60 Hz) due to imbalance between consumed and generated power. The intensity of a light source powered by mains electricity varies depending on the ENF fluctuations. These fluctuations can be extracted from videos recorded in the presence of mains-powered source illumination. This work investigates how the quality of the ENF signal estimated from video is affected by different light source illumination, compression ratios, and by social media encoding.

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Authors:
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon
Submitted On:
15 May 2019 - 8:17am
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[1] Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon, "The Effect of Light Source on ENF Based Video Forensics", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4524. Accessed: May. 23, 2019.
@article{4524-19,
url = {http://sigport.org/4524},
author = {Saffet Vatansever; Ahmet Emir Dirik; Nasir Memon },
publisher = {IEEE SigPort},
title = {The Effect of Light Source on ENF Based Video Forensics},
year = {2019} }
TY - EJOUR
T1 - The Effect of Light Source on ENF Based Video Forensics
AU - Saffet Vatansever; Ahmet Emir Dirik; Nasir Memon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4524
ER -
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. (2019). The Effect of Light Source on ENF Based Video Forensics. IEEE SigPort. http://sigport.org/4524
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon, 2019. The Effect of Light Source on ENF Based Video Forensics. Available at: http://sigport.org/4524.
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. (2019). "The Effect of Light Source on ENF Based Video Forensics." Web.
1. Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. The Effect of Light Source on ENF Based Video Forensics [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4524

Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones


We address the problem of adding new classes to an existing classifier without hurting the original classes, when no access is allowed to any sample from the original classes. This problem arises frequently since models are often shared without their training data, due to privacy and data ownership concerns. We propose an easy-to-use approach that modifies the original classifier by retraining a suitable subset of layers using a linearly-tuned, knowledge-distillation regularization.

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Authors:
Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger
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15 May 2019 - 7:51am
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[1] Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger, "Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4523. Accessed: May. 23, 2019.
@article{4523-19,
url = {http://sigport.org/4523},
author = {Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger },
publisher = {IEEE SigPort},
title = {Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones},
year = {2019} }
TY - EJOUR
T1 - Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones
AU - Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4523
ER -
Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger. (2019). Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones. IEEE SigPort. http://sigport.org/4523
Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger, 2019. Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones. Available at: http://sigport.org/4523.
Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger. (2019). "Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones." Web.
1. Hagai Taitelbaum ; Gal Chechik ; Jacob Goldberger. Network Adaptation Strategies for Learning New Classes without Forgetting the Original Ones [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4523

AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms


This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been outstanding at numerous tasks involving sequence modeling such as speech synthesis and recognition, machine translation, and image captioning.

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Submitted On:
15 May 2019 - 7:03am
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2019_05_ICASSP_KouTanaka.pdf

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[1] , "AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4522. Accessed: May. 23, 2019.
@article{4522-19,
url = {http://sigport.org/4522},
author = { },
publisher = {IEEE SigPort},
title = {AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms},
year = {2019} }
TY - EJOUR
T1 - AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4522
ER -
. (2019). AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms. IEEE SigPort. http://sigport.org/4522
, 2019. AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms. Available at: http://sigport.org/4522.
. (2019). "AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms." Web.
1. . AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4522

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