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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.

Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks


Head movement is an integral part of face-to-face communications. It is important to investigate methodologies to generate naturalistic movements for conversational agents (CAs). The predominant method for head movement generation is using rules based on the meaning of the message. However, the variations of head movements by these methods are bounded by the predefined dictionary of gestures. Speech-driven methods offer an alternative approach, learning the relationship between speech and head movements from real recordings.

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Authors:
Najmeh Sadoughi, Carlos Busso
Submitted On:
1 May 2018 - 8:43pm
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[1] Najmeh Sadoughi, Carlos Busso, "Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3198. Accessed: Sep. 20, 2018.
@article{3198-18,
url = {http://sigport.org/3198},
author = {Najmeh Sadoughi; Carlos Busso },
publisher = {IEEE SigPort},
title = {Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks},
year = {2018} }
TY - EJOUR
T1 - Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks
AU - Najmeh Sadoughi; Carlos Busso
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3198
ER -
Najmeh Sadoughi, Carlos Busso. (2018). Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks. IEEE SigPort. http://sigport.org/3198
Najmeh Sadoughi, Carlos Busso, 2018. Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks. Available at: http://sigport.org/3198.
Najmeh Sadoughi, Carlos Busso. (2018). "Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks." Web.
1. Najmeh Sadoughi, Carlos Busso. Novel Realizations of Speech-driven Head Movements with Generative Adversarial Networks [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3198

FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING


Audio fingerprinting systems are often well designed to cope with a range of broadband noise types however they cope less well when presented with additive noise containing sinusoidal components. This is largely due to the fact that in a short-time signal representa- tion (over periods of ≈ 20ms) these noise components are largely indistinguishable from salient components of the desirable signal that is to be fingerprinted.

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30 April 2018 - 7:27pm
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[1] , "FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3197. Accessed: Sep. 20, 2018.
@article{3197-18,
url = {http://sigport.org/3197},
author = { },
publisher = {IEEE SigPort},
title = {FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING},
year = {2018} }
TY - EJOUR
T1 - FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3197
ER -
. (2018). FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING. IEEE SigPort. http://sigport.org/3197
, 2018. FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING. Available at: http://sigport.org/3197.
. (2018). "FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING." Web.
1. . FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3197

FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING


Audio fingerprinting systems are often well designed to cope with a range of broadband noise types however they cope less well when presented with additive noise containing sinusoidal components. This is largely due to the fact that in a short-time signal representa- tion (over periods of ≈ 20ms) these noise components are largely indistinguishable from salient components of the desirable signal that is to be fingerprinted.

Draft_v2.pdf

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30 April 2018 - 7:27pm
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[1] , "FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3196. Accessed: Sep. 20, 2018.
@article{3196-18,
url = {http://sigport.org/3196},
author = { },
publisher = {IEEE SigPort},
title = {FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING},
year = {2018} }
TY - EJOUR
T1 - FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3196
ER -
. (2018). FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING. IEEE SigPort. http://sigport.org/3196
, 2018. FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING. Available at: http://sigport.org/3196.
. (2018). "FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING." Web.
1. . FOREGROUND HARMONIC NOISE REDUCTION FOR ROBUST AUDIO FINGERPRINTING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3196

Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity


We consider the problem of super-resolution for sub-diffraction imaging. We adapt conventional Fourier ptychographic approaches, for the case where the images to be acquired have an underlying structured sparsity. We propose some sub-sampling strategies which can be easily adapted to existing ptychographic setups. We then use a novel technique called CoPRAM with some modifications, to recover sparse (and block sparse) images from sub-sampled ptychographic measurements.

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Authors:
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani
Submitted On:
30 April 2018 - 2:40pm
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slides-icassp18-nofigs.pdf

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[1] Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3195. Accessed: Sep. 20, 2018.
@article{3195-18,
url = {http://sigport.org/3195},
author = {Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani },
publisher = {IEEE SigPort},
title = {Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity},
year = {2018} }
TY - EJOUR
T1 - Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity
AU - Gauri Jagatap; Zhengyu Chen; Chinmay Hegde; Namrata Vaswani
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3195
ER -
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. IEEE SigPort. http://sigport.org/3195
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani, 2018. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity. Available at: http://sigport.org/3195.
Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. (2018). "Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity." Web.
1. Gauri Jagatap, Zhengyu Chen, Chinmay Hegde, Namrata Vaswani. Sub-diffraction Imaging using Fourier Ptychography and Structured Sparsity [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3195

Study Of Dense Network Approaches For Speech Emotion Recognition

Paper Details

Authors:
Mohammed Abdelwahab, Carlos Busso
Submitted On:
30 April 2018 - 11:45am
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Abdelwahab_ICASSP_2018-poster.pdf

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[1] Mohammed Abdelwahab, Carlos Busso, " Study Of Dense Network Approaches For Speech Emotion Recognition", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3192. Accessed: Sep. 20, 2018.
@article{3192-18,
url = {http://sigport.org/3192},
author = {Mohammed Abdelwahab; Carlos Busso },
publisher = {IEEE SigPort},
title = { Study Of Dense Network Approaches For Speech Emotion Recognition},
year = {2018} }
TY - EJOUR
T1 - Study Of Dense Network Approaches For Speech Emotion Recognition
AU - Mohammed Abdelwahab; Carlos Busso
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3192
ER -
Mohammed Abdelwahab, Carlos Busso. (2018). Study Of Dense Network Approaches For Speech Emotion Recognition. IEEE SigPort. http://sigport.org/3192
Mohammed Abdelwahab, Carlos Busso, 2018. Study Of Dense Network Approaches For Speech Emotion Recognition. Available at: http://sigport.org/3192.
Mohammed Abdelwahab, Carlos Busso. (2018). " Study Of Dense Network Approaches For Speech Emotion Recognition." Web.
1. Mohammed Abdelwahab, Carlos Busso. Study Of Dense Network Approaches For Speech Emotion Recognition [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3192

Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment

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Submitted On:
30 April 2018 - 10:36am
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[1] , "Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3189. Accessed: Sep. 20, 2018.
@article{3189-18,
url = {http://sigport.org/3189},
author = { },
publisher = {IEEE SigPort},
title = {Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment},
year = {2018} }
TY - EJOUR
T1 - Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3189
ER -
. (2018). Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment. IEEE SigPort. http://sigport.org/3189
, 2018. Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment. Available at: http://sigport.org/3189.
. (2018). "Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment." Web.
1. . Speech Prediction using an Adaptive Recurrent Neural Network with Application to Packet Loss Concealment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3189

Unlimited Sampling of Sparse Signals


In a recent paper [1], we introduced the concept of “Unlimited Sampling”. This unique approach circumvents the clipping or saturation problem in conventional analog-to-digital converters (ADCs) by considering a radically different ADC architecture which resets the input voltage before saturation. Such ADCs, also known as Self-Reset ADCs (SR-ADCs), allow for sensing modulo samples.

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Authors:
Felix Krahmer, Ramesh Raskar
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30 April 2018 - 2:45am
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AB_ICASSP 2018.pdf

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[1] Felix Krahmer, Ramesh Raskar, "Unlimited Sampling of Sparse Signals", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3188. Accessed: Sep. 20, 2018.
@article{3188-18,
url = {http://sigport.org/3188},
author = {Felix Krahmer; Ramesh Raskar },
publisher = {IEEE SigPort},
title = {Unlimited Sampling of Sparse Signals},
year = {2018} }
TY - EJOUR
T1 - Unlimited Sampling of Sparse Signals
AU - Felix Krahmer; Ramesh Raskar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3188
ER -
Felix Krahmer, Ramesh Raskar. (2018). Unlimited Sampling of Sparse Signals. IEEE SigPort. http://sigport.org/3188
Felix Krahmer, Ramesh Raskar, 2018. Unlimited Sampling of Sparse Signals. Available at: http://sigport.org/3188.
Felix Krahmer, Ramesh Raskar. (2018). "Unlimited Sampling of Sparse Signals." Web.
1. Felix Krahmer, Ramesh Raskar. Unlimited Sampling of Sparse Signals [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3188

Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody


We describe a new application of deep-learning-based speech synthesis, namely multilingual speech synthesis for generating controllable foreign accent. Specifically, we train a DBLSTM-based acoustic model on non-accented multilingual speech recordings from a speaker native in several languages. By copying durations and pitch contours from a pre-recorded utterance of the desired prompt, natural prosody is achieved. We call this paradigm "cyborg speech" as it combines human and machine speech parameters.

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Authors:
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi
Submitted On:
29 April 2018 - 1:59pm
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[1] Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi, "Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3187. Accessed: Sep. 20, 2018.
@article{3187-18,
url = {http://sigport.org/3187},
author = {Jaime Lorenzo-Trueba; Mariko Kondo; Junichi Yamagishi },
publisher = {IEEE SigPort},
title = {Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody},
year = {2018} }
TY - EJOUR
T1 - Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody
AU - Jaime Lorenzo-Trueba; Mariko Kondo; Junichi Yamagishi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3187
ER -
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. (2018). Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody. IEEE SigPort. http://sigport.org/3187
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi, 2018. Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody. Available at: http://sigport.org/3187.
Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. (2018). "Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody." Web.
1. Jaime Lorenzo-Trueba, Mariko Kondo, Junichi Yamagishi. Cyborg Speech: Deep Multilingual Speech Synthesis for Generating Segmental Foreign Accent with Natural Prosody [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3187

Invisible Geo-Location Signature in a Single Image


Geo-tagging images of interest is increasingly important to law enforcement, national security, and journalism. Many images today do not carry location tags that are trustworthy and resilient to tampering; and the landmark-based visual clues may not be readily present in every image, especially in those taken indoors. In this paper, we exploit an invisible signature from the power grid, the Electric Network Frequency (ENF) signal, which can be inherently recorded in a sensing stream at the time of capturing and carries useful location information.

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27 April 2018 - 6:28pm
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[1] , "Invisible Geo-Location Signature in a Single Image", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3186. Accessed: Sep. 20, 2018.
@article{3186-18,
url = {http://sigport.org/3186},
author = { },
publisher = {IEEE SigPort},
title = {Invisible Geo-Location Signature in a Single Image},
year = {2018} }
TY - EJOUR
T1 - Invisible Geo-Location Signature in a Single Image
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3186
ER -
. (2018). Invisible Geo-Location Signature in a Single Image. IEEE SigPort. http://sigport.org/3186
, 2018. Invisible Geo-Location Signature in a Single Image. Available at: http://sigport.org/3186.
. (2018). "Invisible Geo-Location Signature in a Single Image." Web.
1. . Invisible Geo-Location Signature in a Single Image [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3186

LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS


Information about an image's source camera model is important knowledge in many forensic investigations. In this paper we propose a system that compares two image patches to determine if they were captured by the same camera model. To do this, we first train a CNN based feature extractor to output generic, high level features which encode information about the source camera model of an image patch. Then, we learn a similarity measure that maps pairs of these features to a score indicating whether the two image patches were captured by the same or different camera models.

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Authors:
Owen Mayer, Mathew C. Stamm
Submitted On:
27 April 2018 - 12:45pm
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[1] Owen Mayer, Mathew C. Stamm, "LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3185. Accessed: Sep. 20, 2018.
@article{3185-18,
url = {http://sigport.org/3185},
author = {Owen Mayer; Mathew C. Stamm },
publisher = {IEEE SigPort},
title = {LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS},
year = {2018} }
TY - EJOUR
T1 - LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS
AU - Owen Mayer; Mathew C. Stamm
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3185
ER -
Owen Mayer, Mathew C. Stamm. (2018). LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS. IEEE SigPort. http://sigport.org/3185
Owen Mayer, Mathew C. Stamm, 2018. LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS. Available at: http://sigport.org/3185.
Owen Mayer, Mathew C. Stamm. (2018). "LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS." Web.
1. Owen Mayer, Mathew C. Stamm. LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3185

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