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Audio and Acoustic Signal Processing

Finding Audio Geography using ENF


Electric Network Frequency(ENF) is a recently developed technique for the authentication of audio signals. ENF gets embedded in audio signals due to electromagnetic interferences from power lines and hence can be used as a measure to find the geographical location and time of recording. Given an audio signal, this paper presents a technique of finding the power grid the audio belongs to. Towards this, we first extract the ENF sinusoid using a very narrow bandwidth filter centered around the nominal frequency. The filter is designed using a frequency response masking approach.

Report.pdf

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Authors:
Prerna Singh, Abhinav Jadon, Ambuj Mehrish
Submitted On:
16 June 2016 - 1:57pm
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[1] Prerna Singh, Abhinav Jadon, Ambuj Mehrish, "Finding Audio Geography using ENF", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1124. Accessed: Dec. 16, 2017.
@article{1124-16,
url = {http://sigport.org/1124},
author = {Prerna Singh; Abhinav Jadon; Ambuj Mehrish },
publisher = {IEEE SigPort},
title = {Finding Audio Geography using ENF},
year = {2016} }
TY - EJOUR
T1 - Finding Audio Geography using ENF
AU - Prerna Singh; Abhinav Jadon; Ambuj Mehrish
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1124
ER -
Prerna Singh, Abhinav Jadon, Ambuj Mehrish. (2016). Finding Audio Geography using ENF. IEEE SigPort. http://sigport.org/1124
Prerna Singh, Abhinav Jadon, Ambuj Mehrish, 2016. Finding Audio Geography using ENF. Available at: http://sigport.org/1124.
Prerna Singh, Abhinav Jadon, Ambuj Mehrish. (2016). "Finding Audio Geography using ENF." Web.
1. Prerna Singh, Abhinav Jadon, Ambuj Mehrish. Finding Audio Geography using ENF [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1124

Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith"


This report presents the results to the challenge of
”Exploring Power Signatures for Location Forensics of Media
Recordings” as apart of Signal Processing Cup 2016 by IEEE
Signal Processing Society. Here we examine different frequency
estimation and classification techniques to provide accurate ENF
estimates and classify these signals into the corresponding grid
of recording. In this report we propose methods of efficient
extraction of ENF signal using quadratic interpolation and
frequency tracking.The SVM and GMM classifiers used provided

sp_cup4.pdf

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Authors:
Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P
Submitted On:
24 August 2016 - 1:09am
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[1] Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P, "Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith"", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1121. Accessed: Dec. 16, 2017.
@article{1121-16,
url = {http://sigport.org/1121},
author = {Vineeth Warrier;Harigovind V K;Sreenshan M S;Noufal P },
publisher = {IEEE SigPort},
title = {Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith"},
year = {2016} }
TY - EJOUR
T1 - Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith"
AU - Vineeth Warrier;Harigovind V K;Sreenshan M S;Noufal P
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1121
ER -
Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P. (2016). Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith". IEEE SigPort. http://sigport.org/1121
Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P, 2016. Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith". Available at: http://sigport.org/1121.
Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P. (2016). "Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith"." Web.
1. Vineeth Warrier,Harigovind V K,Sreenshan M S,Noufal P. Exploring Power Signatures for Location Forensics of Media Recordings;SP Cup 2016 Project Report by team "The Zenith" [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1121

Report from UWEngineers for SigCup


Electrical network frequency (ENF) has been used as evidence for location forensic. To determine location, we need accurate ENF information from noisy media files, select features of the ENF signal and the classify it based on previous knowledge of different grids.

UWEngineer.pdf

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Authors:
Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen
Submitted On:
13 June 2016 - 8:39pm
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[1] Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen, "Report from UWEngineers for SigCup ", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1117. Accessed: Dec. 16, 2017.
@article{1117-16,
url = {http://sigport.org/1117},
author = {Qinghua Shen; Wei Zhang; Edrick Wong; Brady Kieffer; Xuemin (Sherman) Shen },
publisher = {IEEE SigPort},
title = {Report from UWEngineers for SigCup },
year = {2016} }
TY - EJOUR
T1 - Report from UWEngineers for SigCup
AU - Qinghua Shen; Wei Zhang; Edrick Wong; Brady Kieffer; Xuemin (Sherman) Shen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1117
ER -
Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen. (2016). Report from UWEngineers for SigCup . IEEE SigPort. http://sigport.org/1117
Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen, 2016. Report from UWEngineers for SigCup . Available at: http://sigport.org/1117.
Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen. (2016). "Report from UWEngineers for SigCup ." Web.
1. Qinghua Shen, Wei Zhang, Edrick Wong, Brady Kieffer, Xuemin (Sherman) Shen. Report from UWEngineers for SigCup [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1117

Linearly Augmented Deep Neural Network


Deep neural networks (DNN) are a powerful tool for many large vocabulary continuous speech recognition (LVCSR) tasks. Training a very deep network is a challenging problem and pre-training techniques are needed in order to achieve the best results. In this paper, we propose a new type of network architecture, Linear Augmented Deep Neural Network (LA-DNN). This type of network augments each non-linear layer with a linear connection from layer input to layer output.

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Authors:
Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer
Submitted On:
30 April 2016 - 7:54pm
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[1] Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer, "Linearly Augmented Deep Neural Network", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1101. Accessed: Dec. 16, 2017.
@article{1101-16,
url = {http://sigport.org/1101},
author = {Pegah Ghahremani; Jasha Droppo; Michael L. Seltzer },
publisher = {IEEE SigPort},
title = {Linearly Augmented Deep Neural Network},
year = {2016} }
TY - EJOUR
T1 - Linearly Augmented Deep Neural Network
AU - Pegah Ghahremani; Jasha Droppo; Michael L. Seltzer
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1101
ER -
Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer. (2016). Linearly Augmented Deep Neural Network. IEEE SigPort. http://sigport.org/1101
Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer, 2016. Linearly Augmented Deep Neural Network. Available at: http://sigport.org/1101.
Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer. (2016). "Linearly Augmented Deep Neural Network." Web.
1. Pegah Ghahremani, Jasha Droppo, Michael L. Seltzer. Linearly Augmented Deep Neural Network [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1101

Recurrent neural networks for polyphonic sound event detection in real life recordings


RECURRENT NEURAL NETWORKS FOR POLYPHONIC SOUND EVENT DETECTION IN REAL LIFE RECORDINGS

Slides from the presentation held at ICASSP 2016 for the paper: Recurrent neural networks for polyphonic sound event detection in real life recordings

Paper Details

Authors:
Heikki Huttunen, Tuomas Virtanen
Submitted On:
4 April 2016 - 9:45am
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[1] Heikki Huttunen, Tuomas Virtanen, "Recurrent neural networks for polyphonic sound event detection in real life recordings", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1082. Accessed: Dec. 16, 2017.
@article{1082-16,
url = {http://sigport.org/1082},
author = {Heikki Huttunen; Tuomas Virtanen },
publisher = {IEEE SigPort},
title = {Recurrent neural networks for polyphonic sound event detection in real life recordings},
year = {2016} }
TY - EJOUR
T1 - Recurrent neural networks for polyphonic sound event detection in real life recordings
AU - Heikki Huttunen; Tuomas Virtanen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1082
ER -
Heikki Huttunen, Tuomas Virtanen. (2016). Recurrent neural networks for polyphonic sound event detection in real life recordings. IEEE SigPort. http://sigport.org/1082
Heikki Huttunen, Tuomas Virtanen, 2016. Recurrent neural networks for polyphonic sound event detection in real life recordings. Available at: http://sigport.org/1082.
Heikki Huttunen, Tuomas Virtanen. (2016). "Recurrent neural networks for polyphonic sound event detection in real life recordings." Web.
1. Heikki Huttunen, Tuomas Virtanen. Recurrent neural networks for polyphonic sound event detection in real life recordings [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1082

Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation

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Authors:
Francois Leduc-Primeau, Warren Gross
Submitted On:
23 March 2016 - 8:21pm
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ICASSP Pres.pdf

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[1] Francois Leduc-Primeau, Warren Gross, "Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1010. Accessed: Dec. 16, 2017.
@article{1010-16,
url = {http://sigport.org/1010},
author = {Francois Leduc-Primeau; Warren Gross },
publisher = {IEEE SigPort},
title = {Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation},
year = {2016} }
TY - EJOUR
T1 - Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation
AU - Francois Leduc-Primeau; Warren Gross
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1010
ER -
Francois Leduc-Primeau, Warren Gross. (2016). Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation. IEEE SigPort. http://sigport.org/1010
Francois Leduc-Primeau, Warren Gross, 2016. Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation. Available at: http://sigport.org/1010.
Francois Leduc-Primeau, Warren Gross. (2016). "Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation." Web.
1. Francois Leduc-Primeau, Warren Gross. Hardware Implementation of FIR/IIR Digital Filters Using Integral Stochastic Computation [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1010

INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION

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Authors:
Mickael Rouvier
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21 March 2016 - 9:02pm
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[1] Mickael Rouvier, "INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/944. Accessed: Dec. 16, 2017.
@article{944-16,
url = {http://sigport.org/944},
author = {Mickael Rouvier },
publisher = {IEEE SigPort},
title = {INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION},
year = {2016} }
TY - EJOUR
T1 - INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION
AU - Mickael Rouvier
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/944
ER -
Mickael Rouvier. (2016). INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION. IEEE SigPort. http://sigport.org/944
Mickael Rouvier, 2016. INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION. Available at: http://sigport.org/944.
Mickael Rouvier. (2016). "INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION." Web.
1. Mickael Rouvier. INVESTIGATION OF SPEAKER EMBEDDINGS FOR CROSS-SHOW SPEAKER DIARIZATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/944

Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise


This paper presents a new method for solving linear inverse problems where the observations are corrupted with a mixed Poisson-Gaussian noise.

slides.pdf

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Authors:
Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet
Submitted On:
21 March 2016 - 7:25pm
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[1] Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet, "Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/939. Accessed: Dec. 16, 2017.
@article{939-16,
url = {http://sigport.org/939},
author = {Yosra Marnissi; Yuling Zheng; and Jean-Christophe Pesquet },
publisher = {IEEE SigPort},
title = {Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise},
year = {2016} }
TY - EJOUR
T1 - Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise
AU - Yosra Marnissi; Yuling Zheng; and Jean-Christophe Pesquet
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/939
ER -
Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet. (2016). Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise. IEEE SigPort. http://sigport.org/939
Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet, 2016. Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise. Available at: http://sigport.org/939.
Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet. (2016). "Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise." Web.
1. Yosra Marnissi, Yuling Zheng, and Jean-Christophe Pesquet. Fast variational Bayesian signal recovery in the presence of Poisson-Gaussian Noise [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/939

Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice


SFA vs MFA

In the state of the art, a single frame of DFT transform is commonly used as a basis for building amplitude spectral envelopes.
Multiple Frame Analysis (MFA) has already been suggested for envelope estimation, but often with excessive complexity.
In this paper, two MFA-based methods are presented: one simplifying an existing Least Square (LS) solution, and another one based on a simple linear interpolation.

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Authors:
Gilles Degottex, Luc Ardaillon, Axel Roebel
Submitted On:
21 March 2016 - 11:37am
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[1] Gilles Degottex, Luc Ardaillon, Axel Roebel, "Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/930. Accessed: Dec. 16, 2017.
@article{930-16,
url = {http://sigport.org/930},
author = {Gilles Degottex; Luc Ardaillon; Axel Roebel },
publisher = {IEEE SigPort},
title = {Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice},
year = {2016} }
TY - EJOUR
T1 - Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice
AU - Gilles Degottex; Luc Ardaillon; Axel Roebel
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/930
ER -
Gilles Degottex, Luc Ardaillon, Axel Roebel. (2016). Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice. IEEE SigPort. http://sigport.org/930
Gilles Degottex, Luc Ardaillon, Axel Roebel, 2016. Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice. Available at: http://sigport.org/930.
Gilles Degottex, Luc Ardaillon, Axel Roebel. (2016). "Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice." Web.
1. Gilles Degottex, Luc Ardaillon, Axel Roebel. Simple Multi Frame Analysis Methods for Estimation of Amplitude Spectral Envelope in Singing Voice [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/930

UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge

Paper Details

Authors:
Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen
Submitted On:
20 March 2016 - 9:54am
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Final-2016-ICASSP-TEMPLATE-POSTER-OneBig-rev1-WIDE-1.ppt

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[1] Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen, "UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/875. Accessed: Dec. 16, 2017.
@article{875-16,
url = {http://sigport.org/875},
author = {Chengzhu Yu; Chunlei Zhang; Shivesh Ranjan; Qian Zhang; Abhinav Misra; Finnian Kelly; and John H.L. Hansen },
publisher = {IEEE SigPort},
title = {UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge},
year = {2016} }
TY - EJOUR
T1 - UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge
AU - Chengzhu Yu; Chunlei Zhang; Shivesh Ranjan; Qian Zhang; Abhinav Misra; Finnian Kelly; and John H.L. Hansen
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/875
ER -
Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen. (2016). UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge. IEEE SigPort. http://sigport.org/875
Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen, 2016. UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge. Available at: http://sigport.org/875.
Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen. (2016). "UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge." Web.
1. Chengzhu Yu, Chunlei Zhang, Shivesh Ranjan, Qian Zhang, Abhinav Misra, Finnian Kelly, and John H.L. Hansen. UTD-CRSS System For The NIST 2015 Language Recognition I-Vector Machine Learning Challenge [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/875

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