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Audio and Acoustic Signal Processing

Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing


Processing of speech and audio signals with time-frequency representations require windowing methods which allow perfect reconstruction of the original signal and where processing artifacts have a predictable behavior. The most common approach for this purpose is overlap-add windowing, where signal segments are windowed before and after processing. Commonly used windows include the half-sine and a Kaiser-Bessel derived window. The latter is an approximation of the discrete prolate spherical sequence, and thus a maximum energy concentration window, adapted for overlap-add.

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8 May 2019 - 2:16am
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[1] , "Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4019. Accessed: Jun. 26, 2019.
@article{4019-19,
url = {http://sigport.org/4019},
author = { },
publisher = {IEEE SigPort},
title = {Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing},
year = {2019} }
TY - EJOUR
T1 - Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4019
ER -
. (2019). Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing. IEEE SigPort. http://sigport.org/4019
, 2019. Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing. Available at: http://sigport.org/4019.
. (2019). "Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing." Web.
1. . Overlap-Add Windows with Maximum Energy Concentration for Speech and Audio Processing [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4019

FINE-TUNING APPROACH TO NIR FACE RECOGNITION


Despite extensive researches for face recognition (FR), it is still difficult to apply deep CNN models to NIR FR due to a lack of training data. In this study, we propose a fine-tuning approach to allow deep CNN models to be applied to NIR FR with small training datasets. In the proposed approach, parameters of deep CNN models for RGB FR are utilized as initial parameters to train deep CNN models for NIR FR. The proposed approach has two main advantages: 1) High NIR FR performances can be achieved with very small public training datasets.

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Authors:
Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim
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10 May 2019 - 3:10am
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[1] Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim, "FINE-TUNING APPROACH TO NIR FACE RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4010. Accessed: Jun. 26, 2019.
@article{4010-19,
url = {http://sigport.org/4010},
author = {Jeyeon Kim; Hoon Jo; Moonsoo Ra; Whoi-Yul Kim },
publisher = {IEEE SigPort},
title = {FINE-TUNING APPROACH TO NIR FACE RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - FINE-TUNING APPROACH TO NIR FACE RECOGNITION
AU - Jeyeon Kim; Hoon Jo; Moonsoo Ra; Whoi-Yul Kim
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4010
ER -
Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim. (2019). FINE-TUNING APPROACH TO NIR FACE RECOGNITION. IEEE SigPort. http://sigport.org/4010
Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim, 2019. FINE-TUNING APPROACH TO NIR FACE RECOGNITION. Available at: http://sigport.org/4010.
Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim. (2019). "FINE-TUNING APPROACH TO NIR FACE RECOGNITION." Web.
1. Jeyeon Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim. FINE-TUNING APPROACH TO NIR FACE RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4010

DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST

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7 May 2019 - 10:40pm
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[1] , "DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3995. Accessed: Jun. 26, 2019.
@article{3995-19,
url = {http://sigport.org/3995},
author = { },
publisher = {IEEE SigPort},
title = {DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST},
year = {2019} }
TY - EJOUR
T1 - DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3995
ER -
. (2019). DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST. IEEE SigPort. http://sigport.org/3995
, 2019. DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST. Available at: http://sigport.org/3995.
. (2019). "DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST." Web.
1. . DIFFERENTIALLY PRIVATE GREEDY DECISION FOREST [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3995

COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION


Despite the great advances, most of the recently developed automatic speech recognition systems focus on working in a server-client manner, and thus often require a high computational cost, such as the storage size and memory accesses. This, however, does not satisfy the increasing demand for a succinct model that can run smoothly in embedded devices like smartphones.

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Authors:
Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang
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7 May 2019 - 7:10pm
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[1] Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang, "COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3967. Accessed: Jun. 26, 2019.
@article{3967-19,
url = {http://sigport.org/3967},
author = {Huan Zhao; Yufeng Xiao; Jing Han; Zixing Zhang },
publisher = {IEEE SigPort},
title = {COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION},
year = {2019} }
TY - EJOUR
T1 - COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION
AU - Huan Zhao; Yufeng Xiao; Jing Han; Zixing Zhang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3967
ER -
Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang. (2019). COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION. IEEE SigPort. http://sigport.org/3967
Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang, 2019. COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION. Available at: http://sigport.org/3967.
Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang. (2019). "COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION." Web.
1. Huan Zhao, Yufeng Xiao, Jing Han, Zixing Zhang. COMPACT CONVOLUTIONAL RECURRENT NEURAL NETWORKS VIA BINARIZATION FOR SPEECH EMOTION RECOGNITION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3967

ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)

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Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps
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7 May 2019 - 6:58pm
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[1] Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3966. Accessed: Jun. 26, 2019.
@article{3966-19,
url = {http://sigport.org/3966},
author = {Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)
AU - Tharshini Gunendradasan; Saad Irtza; Eliathamby Ambikairajah; Julien Epps
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3966
ER -
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). IEEE SigPort. http://sigport.org/3966
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps, 2019. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION). Available at: http://sigport.org/3966.
Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. (2019). "ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION)." Web.
1. Tharshini Gunendradasan, Saad Irtza, Eliathamby Ambikairajah, Julien Epps. ICASSP 2019 Poster (TRANSMISSION LINE COCHLEAR MODEL BASED AM-FM FEATURES FOR REPLAY ATTACK DETECTION) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3966

DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM

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7 May 2019 - 5:46pm
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[1] , "DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3962. Accessed: Jun. 26, 2019.
@article{3962-19,
url = {http://sigport.org/3962},
author = { },
publisher = {IEEE SigPort},
title = {DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM},
year = {2019} }
TY - EJOUR
T1 - DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3962
ER -
. (2019). DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM. IEEE SigPort. http://sigport.org/3962
, 2019. DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM. Available at: http://sigport.org/3962.
. (2019). "DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM." Web.
1. . DISTRIBUTED TRACKING OF MANEUVERING TARGET: A FINITE-TIME ALGORITHM [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3962

Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach


Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models.

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Sajad Mousavi , Fatemeh Afghah
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7 May 2019 - 3:03pm
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[1] Sajad Mousavi , Fatemeh Afghah, "Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3947. Accessed: Jun. 26, 2019.
@article{3947-19,
url = {http://sigport.org/3947},
author = {Sajad Mousavi ; Fatemeh Afghah },
publisher = {IEEE SigPort},
title = {Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach},
year = {2019} }
TY - EJOUR
T1 - Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach
AU - Sajad Mousavi ; Fatemeh Afghah
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3947
ER -
Sajad Mousavi , Fatemeh Afghah. (2019). Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach. IEEE SigPort. http://sigport.org/3947
Sajad Mousavi , Fatemeh Afghah, 2019. Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach. Available at: http://sigport.org/3947.
Sajad Mousavi , Fatemeh Afghah. (2019). "Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach." Web.
1. Sajad Mousavi , Fatemeh Afghah. Inter- and Intra- Patient ECG Heartbeat Classification For Arrhythmia Detection: a Sequence to Sequence Deep Learning Approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3947

Bluetooth based Indoor Localization using Triplet Embeddings

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Karel Mundnich, Benjamin Girault, Shrikanth Narayanan
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7 May 2019 - 2:43pm
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[1] Karel Mundnich, Benjamin Girault, Shrikanth Narayanan, "Bluetooth based Indoor Localization using Triplet Embeddings", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3945. Accessed: Jun. 26, 2019.
@article{3945-19,
url = {http://sigport.org/3945},
author = {Karel Mundnich; Benjamin Girault; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Bluetooth based Indoor Localization using Triplet Embeddings},
year = {2019} }
TY - EJOUR
T1 - Bluetooth based Indoor Localization using Triplet Embeddings
AU - Karel Mundnich; Benjamin Girault; Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3945
ER -
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. (2019). Bluetooth based Indoor Localization using Triplet Embeddings. IEEE SigPort. http://sigport.org/3945
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan, 2019. Bluetooth based Indoor Localization using Triplet Embeddings. Available at: http://sigport.org/3945.
Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. (2019). "Bluetooth based Indoor Localization using Triplet Embeddings." Web.
1. Karel Mundnich, Benjamin Girault, Shrikanth Narayanan. Bluetooth based Indoor Localization using Triplet Embeddings [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3945

DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS


In this paper, we propose a statistical framework to prune feature maps in 1-D deep convolutional networks. SoundNet is a pre-trained deep convolutional network that accepts raw audio samples as input. The feature maps generated at various layers of SoundNet have redundancy, which can be identified by statistical analysis. These redundant feature maps can be pruned from the network with a very minor reduction in the capability of the network.

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Authors:
Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar
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7 May 2019 - 1:19am
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[1] Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar , "DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3919. Accessed: Jun. 26, 2019.
@article{3919-19,
url = {http://sigport.org/3919},
author = { Arshdeep Singh ; Padmanabhan Rajan ; Arnav Bhavsar },
publisher = {IEEE SigPort},
title = {DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS},
year = {2019} }
TY - EJOUR
T1 - DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS
AU - Arshdeep Singh ; Padmanabhan Rajan ; Arnav Bhavsar
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3919
ER -
Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar . (2019). DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS. IEEE SigPort. http://sigport.org/3919
Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar , 2019. DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS. Available at: http://sigport.org/3919.
Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar . (2019). "DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS." Web.
1. Arshdeep Singh , Padmanabhan Rajan , Arnav Bhavsar . DEEP HIDDEN ANALYSIS: A STATISTICAL FRAMEWORK TO PRUNE FEATURE MAPS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3919

Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game


This paper studies the problem of Stackelberg game based distributed power allocation for spectral coexisting multistatic radar and communication systems. The strategy aims to minimize the radiated power of each radar by optimizing transmit power allocation for a desired signal-to-interference-plus-noise ratio (SINR) meanwhile the communication base station (CBS) is protected from the interference of radar transmissions. We formulate this distributed power allocation process as a Stackelberg game, where the CBS is a leader and the radars are the followers.

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Authors:
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou
Submitted On:
14 February 2019 - 10:01pm
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[1] Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou, "Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3850. Accessed: Jun. 26, 2019.
@article{3850-19,
url = {http://sigport.org/3850},
author = {Chenguang Shi; Fei Wang; Sana Salous; Jianjiang Zhou },
publisher = {IEEE SigPort},
title = {Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game},
year = {2019} }
TY - EJOUR
T1 - Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game
AU - Chenguang Shi; Fei Wang; Sana Salous; Jianjiang Zhou
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3850
ER -
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. (2019). Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game. IEEE SigPort. http://sigport.org/3850
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou, 2019. Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game. Available at: http://sigport.org/3850.
Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. (2019). "Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game." Web.
1. Chenguang Shi, Fei Wang, Sana Salous, Jianjiang Zhou. Distributed Power Allocation for Spectral Coexisting Multistatic Radar and Communication Systems Based on Stackelberg Game [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3850

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