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Audio and Acoustic Signal Processing

Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation

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Authors:
Amarjot Singh, Nick Kingsbury
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14 April 2018 - 3:41pm
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[1] Amarjot Singh, Nick Kingsbury, "Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2850. Accessed: Sep. 22, 2018.
@article{2850-18,
url = {http://sigport.org/2850},
author = {Amarjot Singh; Nick Kingsbury },
publisher = {IEEE SigPort},
title = {Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation},
year = {2018} }
TY - EJOUR
T1 - Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation
AU - Amarjot Singh; Nick Kingsbury
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2850
ER -
Amarjot Singh, Nick Kingsbury. (2018). Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation. IEEE SigPort. http://sigport.org/2850
Amarjot Singh, Nick Kingsbury, 2018. Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation. Available at: http://sigport.org/2850.
Amarjot Singh, Nick Kingsbury. (2018). "Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation." Web.
1. Amarjot Singh, Nick Kingsbury. Generative ScatterNet Hybrid Deep Learning (G-SHDL) Network with Structural priors for Semantic Image Segmentation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2850

High Accuracy Acoustic Estimation of Multiple Targets


This paper presents a new adaptation of a Gaussian echo model (GEM) to estimate the distances to multiple targets using acoustic signals. The proposed algorithm utilizes m-sequences and opens the door for applying other modulations and signal designs for acoustic estimation in a similar way. The proposed algorithm estimates the system impulse response and uses the GEM to limit the effect of noise before applying deconvolution to estimate the time of arrival (TOA) to multiple targets with high accuracy.

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Authors:
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri
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14 April 2018 - 12:07pm
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High Accuracy Acoustic Estimation of Multiple Targets_Poster.pdf

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[1] Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri, "High Accuracy Acoustic Estimation of Multiple Targets", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2839. Accessed: Sep. 22, 2018.
@article{2839-18,
url = {http://sigport.org/2839},
author = {Mohammed H. AlSharif; Mohamed Saad; Mohamed Siala; Hatem Boujemaa; Tarig Ballal; Tareq Y. Al-Naffouri },
publisher = {IEEE SigPort},
title = {High Accuracy Acoustic Estimation of Multiple Targets},
year = {2018} }
TY - EJOUR
T1 - High Accuracy Acoustic Estimation of Multiple Targets
AU - Mohammed H. AlSharif; Mohamed Saad; Mohamed Siala; Hatem Boujemaa; Tarig Ballal; Tareq Y. Al-Naffouri
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2839
ER -
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). High Accuracy Acoustic Estimation of Multiple Targets. IEEE SigPort. http://sigport.org/2839
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri, 2018. High Accuracy Acoustic Estimation of Multiple Targets. Available at: http://sigport.org/2839.
Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. (2018). "High Accuracy Acoustic Estimation of Multiple Targets." Web.
1. Mohammed H. AlSharif, Mohamed Saad, Mohamed Siala, Hatem Boujemaa, Tarig Ballal, Tareq Y. Al-Naffouri. High Accuracy Acoustic Estimation of Multiple Targets [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2839

NEURAL ADAPTIVE IMAGE DENOISER


We propose a novel neural network-based adaptive image denoiser, dubbased as Neural AIDE. Unlike other neural network-based denoisers, which typically apply supervised training to learn a mapping from a noisy patch to a clean patch, we formulate to train a neural network to learn context- based affine mappings that get applied to each noisy pixel. Our formulation enables using SURE (Stein’s Unbiased Risk Estimator)-like estimated losses of those mappings as empirical risks to minimize.

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Authors:
Sungmin Cha, Taesup Moon
Submitted On:
14 April 2018 - 8:37am
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NAIDE_Poster_ICASSP2018

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[1] Sungmin Cha, Taesup Moon, "NEURAL ADAPTIVE IMAGE DENOISER", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2825. Accessed: Sep. 22, 2018.
@article{2825-18,
url = {http://sigport.org/2825},
author = {Sungmin Cha; Taesup Moon },
publisher = {IEEE SigPort},
title = {NEURAL ADAPTIVE IMAGE DENOISER},
year = {2018} }
TY - EJOUR
T1 - NEURAL ADAPTIVE IMAGE DENOISER
AU - Sungmin Cha; Taesup Moon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2825
ER -
Sungmin Cha, Taesup Moon. (2018). NEURAL ADAPTIVE IMAGE DENOISER. IEEE SigPort. http://sigport.org/2825
Sungmin Cha, Taesup Moon, 2018. NEURAL ADAPTIVE IMAGE DENOISER. Available at: http://sigport.org/2825.
Sungmin Cha, Taesup Moon. (2018). "NEURAL ADAPTIVE IMAGE DENOISER." Web.
1. Sungmin Cha, Taesup Moon. NEURAL ADAPTIVE IMAGE DENOISER [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2825

SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS


Motivation:
Speech signal is an important information carrier in many social applications such as WeChat and GoogleTalk;
Modern digital technologies have put the security of speech at risk.
Solution: Watermarking is a promising solution to protect the speech signals by embedding digital data into them [1, 2].
Problem:
Many existing methods cannot satisfy the requirements of watermarking, e.g., inaudibility and robustness, simultaneously;

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Authors:
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI
Submitted On:
14 April 2018 - 6:16am
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ICASSP2018_Poster.pdf

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[1] Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2814. Accessed: Sep. 22, 2018.
@article{2814-18,
url = {http://sigport.org/2814},
author = {Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI },
publisher = {IEEE SigPort},
title = {SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS},
year = {2018} }
TY - EJOUR
T1 - SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS
AU - Shengbei WANG; Weitao YUAN; Jianming WANG; Masashi UNOKI
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2814
ER -
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. IEEE SigPort. http://sigport.org/2814
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI, 2018. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS. Available at: http://sigport.org/2814.
Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. (2018). "SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS." Web.
1. Shengbei WANG, Weitao YUAN, Jianming WANG, Masashi UNOKI. SPEECH WATERMARKING BASED ON ROBUST PRINCIPAL COMPONENT ANALYSIS AND FORMANT MANIPULATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2814

A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios

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Authors:
Tobias Weber, Anja Klein
Submitted On:
14 April 2018 - 3:14am
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BC_ICASSP.pdf

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[1] Tobias Weber, Anja Klein, "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2807. Accessed: Sep. 22, 2018.
@article{2807-18,
url = {http://sigport.org/2807},
author = {Tobias Weber; Anja Klein },
publisher = {IEEE SigPort},
title = {A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios},
year = {2018} }
TY - EJOUR
T1 - A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios
AU - Tobias Weber; Anja Klein
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2807
ER -
Tobias Weber, Anja Klein. (2018). A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. IEEE SigPort. http://sigport.org/2807
Tobias Weber, Anja Klein, 2018. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios. Available at: http://sigport.org/2807.
Tobias Weber, Anja Klein. (2018). "A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios." Web.
1. Tobias Weber, Anja Klein. A Two-Layer Reinforcement Learning Solution for Energy Harvesting Data Dissemination Scenarios [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2807

COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL


Specific emitter identification (SEI) is gaining popularity since it can distinguish different individuals in same type of radar emitter under complex electromagnetic environment. However, classification of signals is still a challenging task when the feature has low physical representation. In this work, we propose a compressed sensing mask feature in ambiguity domain, which can significantly improve the recognition rate of civil flight radar emitters.

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Authors:
Xinliang Zhang, Yue Qi, Hongbing Ji
Submitted On:
14 April 2018 - 3:03am
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ICASSP poster.pdf

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[1] Xinliang Zhang, Yue Qi, Hongbing Ji, "COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2805. Accessed: Sep. 22, 2018.
@article{2805-18,
url = {http://sigport.org/2805},
author = {Xinliang Zhang; Yue Qi; Hongbing Ji },
publisher = {IEEE SigPort},
title = {COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL},
year = {2018} }
TY - EJOUR
T1 - COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL
AU - Xinliang Zhang; Yue Qi; Hongbing Ji
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2805
ER -
Xinliang Zhang, Yue Qi, Hongbing Ji. (2018). COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL. IEEE SigPort. http://sigport.org/2805
Xinliang Zhang, Yue Qi, Hongbing Ji, 2018. COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL. Available at: http://sigport.org/2805.
Xinliang Zhang, Yue Qi, Hongbing Ji. (2018). "COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL." Web.
1. Xinliang Zhang, Yue Qi, Hongbing Ji. COMPRESSED SENSING MASK FEATURE IN TIME-FREQUENCY DOMAIN FOR CIVIL [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2805

Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning

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14 April 2018 - 5:02am
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Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning.pdf

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[1] , "Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2804. Accessed: Sep. 22, 2018.
@article{2804-18,
url = {http://sigport.org/2804},
author = { },
publisher = {IEEE SigPort},
title = {Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning},
year = {2018} }
TY - EJOUR
T1 - Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2804
ER -
. (2018). Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning. IEEE SigPort. http://sigport.org/2804
, 2018. Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning. Available at: http://sigport.org/2804.
. (2018). "Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning." Web.
1. . Sparse Recovery Assisted DOA Estimation Utilizing Sparse Bayesian Learning [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2804

Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings


The detection of overlapping speech segments is of key importance in speech applications involving analysis of multi-party conversations. The detection problem is challenging because overlapping speech segments are typically captured as short speech utterances far-field microphone recordings. In this paper, we propose detection of overlap segments using a neural network architecture consisting of long-short term memory (LSTM) models. The neural network architecture learns the presence of overlap in speech by identifying the spectrotemporal structure of overlapping speech segments.

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Authors:
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant
Submitted On:
14 April 2018 - 2:54am
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[1] Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant, "Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2803. Accessed: Sep. 22, 2018.
@article{2803-18,
url = {http://sigport.org/2803},
author = {Neeraj Sajjan; Shobhana Ganesh; Neeraj Sharma; Sriram Ganapathy; Neville Ryant },
publisher = {IEEE SigPort},
title = {Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings},
year = {2018} }
TY - EJOUR
T1 - Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings
AU - Neeraj Sajjan; Shobhana Ganesh; Neeraj Sharma; Sriram Ganapathy; Neville Ryant
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2803
ER -
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. (2018). Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings. IEEE SigPort. http://sigport.org/2803
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant, 2018. Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings. Available at: http://sigport.org/2803.
Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. (2018). "Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings." Web.
1. Neeraj Sajjan, Shobhana Ganesh, Neeraj Sharma, Sriram Ganapathy, Neville Ryant. Leveraging LSTM Models for Overlap Detection in Multi-Party Meetings [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2803

END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG

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Authors:
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung
Submitted On:
14 April 2018 - 2:48am
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wu poster

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[1] Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung, "END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2801. Accessed: Sep. 22, 2018.
@article{2801-18,
url = {http://sigport.org/2801},
author = {Chien-Sheng Wu; Andrea Madotto; Genta Winata; Pascale Fung },
publisher = {IEEE SigPort},
title = {END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG},
year = {2018} }
TY - EJOUR
T1 - END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG
AU - Chien-Sheng Wu; Andrea Madotto; Genta Winata; Pascale Fung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2801
ER -
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. (2018). END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG. IEEE SigPort. http://sigport.org/2801
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung, 2018. END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG. Available at: http://sigport.org/2801.
Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. (2018). "END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG." Web.
1. Chien-Sheng Wu, Andrea Madotto, Genta Winata, Pascale Fung. END-TO-END DYNAMIC QUERY MEMORY NETWORK FOR ENTITY-VALUE INDEPENDENT TASK-ORIENTED DIALOG [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2801

MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION

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Authors:
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal
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14 April 2018 - 2:22am
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[1] Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal, "MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2800. Accessed: Sep. 22, 2018.
@article{2800-18,
url = {http://sigport.org/2800},
author = {Minhua Wu; Sankaran Panchapagesan; Ming Sun; Jiacheng Gu; Ryan Thomas; Shiv Naga Prasad Vitaladevuni; Bjorn Hoffmeister; Arindam Mandal },
publisher = {IEEE SigPort},
title = {MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION},
year = {2018} }
TY - EJOUR
T1 - MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION
AU - Minhua Wu; Sankaran Panchapagesan; Ming Sun; Jiacheng Gu; Ryan Thomas; Shiv Naga Prasad Vitaladevuni; Bjorn Hoffmeister; Arindam Mandal
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2800
ER -
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. (2018). MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION. IEEE SigPort. http://sigport.org/2800
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal, 2018. MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION. Available at: http://sigport.org/2800.
Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. (2018). "MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION." Web.
1. Minhua Wu, Sankaran Panchapagesan, Ming Sun, Jiacheng Gu, Ryan Thomas, Shiv Naga Prasad Vitaladevuni, Bjorn Hoffmeister, Arindam Mandal. MONOPHONE-BASED BACKGROUND MODELING FOR TWO-STAGE ON-DEVICE WAKE WORD DETECTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2800

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