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Signal and System Modeling, Representation and Estimation

COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT

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Authors:
Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic
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13 April 2018 - 10:51am
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CPDFT_ICASSP.pdf

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[1] Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic, "COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2713. Accessed: Jul. 16, 2019.
@article{2713-18,
url = {http://sigport.org/2713},
author = {Bruno Scalzo Dees; Scott C. Douglas; Danilo P. Mandic },
publisher = {IEEE SigPort},
title = {COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT},
year = {2018} }
TY - EJOUR
T1 - COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT
AU - Bruno Scalzo Dees; Scott C. Douglas; Danilo P. Mandic
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2713
ER -
Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic. (2018). COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT. IEEE SigPort. http://sigport.org/2713
Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic, 2018. COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT. Available at: http://sigport.org/2713.
Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic. (2018). "COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT." Web.
1. Bruno Scalzo Dees, Scott C. Douglas, Danilo P. Mandic. COMPLEMENTARY COMPLEX-VALUED SPECTRUM FOR REAL-VALUED DATA: REAL TIME ESTIMATION OF THE PANORAMA THROUGH CIRCULARITY-PRESERVING DFT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2713

On the Computability of System Approximations under Causality Constraints


Approximating the transfer function of stable causal linear systems by a basis expansion is a common task in signal- and system theory. This paper characterizes a scale of signal spaces, containing stable causal transfer functions, with a very simple basis (the Fourier basis) but which is not computable. Thus it is not possible to determine the coefficients of this basis expansion on any digital computer such that the approximation converges to the desired function.

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Authors:
Holger Boche, Volker Pohl
Submitted On:
13 April 2018 - 6:04am
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BochePohl_ICASSP18.pdf

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[1] Holger Boche, Volker Pohl, "On the Computability of System Approximations under Causality Constraints", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2677. Accessed: Jul. 16, 2019.
@article{2677-18,
url = {http://sigport.org/2677},
author = {Holger Boche; Volker Pohl },
publisher = {IEEE SigPort},
title = {On the Computability of System Approximations under Causality Constraints},
year = {2018} }
TY - EJOUR
T1 - On the Computability of System Approximations under Causality Constraints
AU - Holger Boche; Volker Pohl
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2677
ER -
Holger Boche, Volker Pohl. (2018). On the Computability of System Approximations under Causality Constraints. IEEE SigPort. http://sigport.org/2677
Holger Boche, Volker Pohl, 2018. On the Computability of System Approximations under Causality Constraints. Available at: http://sigport.org/2677.
Holger Boche, Volker Pohl. (2018). "On the Computability of System Approximations under Causality Constraints." Web.
1. Holger Boche, Volker Pohl. On the Computability of System Approximations under Causality Constraints [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2677

LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING

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13 April 2018 - 4:13am
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yanbin_ICASSP2018-ppt.pdf

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[1] , "LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2649. Accessed: Jul. 16, 2019.
@article{2649-18,
url = {http://sigport.org/2649},
author = { },
publisher = {IEEE SigPort},
title = {LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING},
year = {2018} }
TY - EJOUR
T1 - LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2649
ER -
. (2018). LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING. IEEE SigPort. http://sigport.org/2649
, 2018. LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING. Available at: http://sigport.org/2649.
. (2018). "LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING." Web.
1. . LOW-RANK AND JOINT-SPARSE SIGNAL RECOVERY FOR SPATIALLY AND TEMPORALLY CORRELATED DATA USING SPARSE BAYESIAN LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2649

PhaseSplit: A Variable Splitting Framework for Phase Retrieval


We develop two techniques based on alternating minimization and
alternating directions method of multipliers for phase retrieval (PR)
by employing a variable-splitting approach in a maximum likelihood
estimation framework. This leads to an additional equality constraint,
which is incorporated in the optimization framework using a
quadratic penalty. Both algorithms are iterative, wherein the updates
are computed in closed-form. Experimental results show that: (i)
the proposed techniques converge faster than the state-of-the-art PR

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Authors:
Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula
Submitted On:
13 April 2018 - 1:11am
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icassp2018_poster.pdf

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[1] Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula, "PhaseSplit: A Variable Splitting Framework for Phase Retrieval", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2599. Accessed: Jul. 16, 2019.
@article{2599-18,
url = {http://sigport.org/2599},
author = {Subhadip Mukherjee; Suprosanna Shit; and Chandra Sekhar Seelamantula },
publisher = {IEEE SigPort},
title = {PhaseSplit: A Variable Splitting Framework for Phase Retrieval},
year = {2018} }
TY - EJOUR
T1 - PhaseSplit: A Variable Splitting Framework for Phase Retrieval
AU - Subhadip Mukherjee; Suprosanna Shit; and Chandra Sekhar Seelamantula
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2599
ER -
Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula. (2018). PhaseSplit: A Variable Splitting Framework for Phase Retrieval. IEEE SigPort. http://sigport.org/2599
Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula, 2018. PhaseSplit: A Variable Splitting Framework for Phase Retrieval. Available at: http://sigport.org/2599.
Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula. (2018). "PhaseSplit: A Variable Splitting Framework for Phase Retrieval." Web.
1. Subhadip Mukherjee, Suprosanna Shit, and Chandra Sekhar Seelamantula. PhaseSplit: A Variable Splitting Framework for Phase Retrieval [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2599

UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE


Uncertainty principles in finite dimensional vector space have been studied extensively, however they cannot be applied to sparse representation of rational functions. This paper considers the sparse representation for a rational function under a pair of orthonormal rational function bases. We prove the uncertainty principle concerning pairs of compressible representation of rational functions in the infinite dimensional function space. The uniqueness of compressible representation using such pairs is provided as a direct consequence of uncertainty principle.

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Authors:
Li Chai, Jingxin Zhang
Submitted On:
13 April 2018 - 12:55am
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[1] Li Chai, Jingxin Zhang, "UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2595. Accessed: Jul. 16, 2019.
@article{2595-18,
url = {http://sigport.org/2595},
author = {Li Chai; Jingxin Zhang },
publisher = {IEEE SigPort},
title = {UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE},
year = {2018} }
TY - EJOUR
T1 - UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE
AU - Li Chai; Jingxin Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2595
ER -
Li Chai, Jingxin Zhang. (2018). UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE. IEEE SigPort. http://sigport.org/2595
Li Chai, Jingxin Zhang, 2018. UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE. Available at: http://sigport.org/2595.
Li Chai, Jingxin Zhang. (2018). "UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE." Web.
1. Li Chai, Jingxin Zhang. UNCERTAINTY PRINCIPLE FOR RATIONAL FUNCTIONS IN HARDY SPACE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2595

SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS


In this paper, we propose single depth image super-resolution using convolutional neural networks (CNN). We adopt CNN to acquire a high-quality edge map from the input low-resolution (LR) depth image. We use the high-quality edge map as the weight of the regularization term in a total variation (TV) model for super-resolution. First, we interpolate the LR depth image using bicubic interpolation and extract its low-quality edge map. Then, we get the high-quality edge map from the low-quality one using CNN.

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Authors:
Cheolkon Jung
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12 April 2018 - 11:53pm
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ICASSP2018poster_Depth_rev_final .pdf

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[1] Cheolkon Jung, "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2577. Accessed: Jul. 16, 2019.
@article{2577-18,
url = {http://sigport.org/2577},
author = {Cheolkon Jung },
publisher = {IEEE SigPort},
title = {SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS },
year = {2018} }
TY - EJOUR
T1 - SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS
AU - Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2577
ER -
Cheolkon Jung. (2018). SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . IEEE SigPort. http://sigport.org/2577
Cheolkon Jung, 2018. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . Available at: http://sigport.org/2577.
Cheolkon Jung. (2018). "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ." Web.
1. Cheolkon Jung. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2577

ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE


Estimating envelope of a signal has various applications including empirical mode decomposition (EMD) in which the cubic $C^2$-spline based envelope estimation is generally used. While such functional approach can easily control smoothness of an estimated envelope, the so-called undershoot problem often occurs that violates the basic requirement of envelope. In this paper, a tangentially constrained spline with tangential points optimization is proposed for avoiding the undershoot problem while maintaining smoothness.

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Authors:
Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa
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12 April 2018 - 11:45pm
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[1] Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa, "ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2575. Accessed: Jul. 16, 2019.
@article{2575-18,
url = {http://sigport.org/2575},
author = {Tsubasa Kusano; Kohei Yatabe; Yasuhiro Oikawa },
publisher = {IEEE SigPort},
title = {ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE},
year = {2018} }
TY - EJOUR
T1 - ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE
AU - Tsubasa Kusano; Kohei Yatabe; Yasuhiro Oikawa
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2575
ER -
Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa. (2018). ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE. IEEE SigPort. http://sigport.org/2575
Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa, 2018. ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE. Available at: http://sigport.org/2575.
Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa. (2018). "ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE." Web.
1. Tsubasa Kusano, Kohei Yatabe, Yasuhiro Oikawa. ENVELOPE ESTIMATION BY TANGENTIALLY CONSTRAINED SPLINE [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2575

SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS


In this paper, a novel framework for the single depth image superresolution is proposed. In our framework, we first extract a low-quality edge map from an interpolated depth map.Then we transform the low-quality edge map to a high quality one by our trained deep convolution neural network (CNN) with two-step postprocessing. Guided by the high-quality edge map, we finally utilize a total variation (TV) based model to upsample the initial depth map.

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Authors:
Cheolkon Jung
Submitted On:
12 April 2018 - 11:53pm
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ICASSP2018poster_Depth_rev_final.pdf

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[1] Cheolkon Jung, "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2573. Accessed: Jul. 16, 2019.
@article{2573-18,
url = {http://sigport.org/2573},
author = {Cheolkon Jung },
publisher = {IEEE SigPort},
title = {SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS },
year = {2018} }
TY - EJOUR
T1 - SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS
AU - Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2573
ER -
Cheolkon Jung. (2018). SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . IEEE SigPort. http://sigport.org/2573
Cheolkon Jung, 2018. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS . Available at: http://sigport.org/2573.
Cheolkon Jung. (2018). "SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS ." Web.
1. Cheolkon Jung. SINGLE DEPTH IMAGE SUPER-RESOLUTION USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2573

privacy-aware kalman filter poster

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Authors:
Yang Song, Chong Xiao Wang, Wee Peng Tay
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12 April 2018 - 9:30pm
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[1] Yang Song, Chong Xiao Wang, Wee Peng Tay, "privacy-aware kalman filter poster", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2541. Accessed: Jul. 16, 2019.
@article{2541-18,
url = {http://sigport.org/2541},
author = {Yang Song; Chong Xiao Wang; Wee Peng Tay },
publisher = {IEEE SigPort},
title = {privacy-aware kalman filter poster},
year = {2018} }
TY - EJOUR
T1 - privacy-aware kalman filter poster
AU - Yang Song; Chong Xiao Wang; Wee Peng Tay
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2541
ER -
Yang Song, Chong Xiao Wang, Wee Peng Tay. (2018). privacy-aware kalman filter poster. IEEE SigPort. http://sigport.org/2541
Yang Song, Chong Xiao Wang, Wee Peng Tay, 2018. privacy-aware kalman filter poster. Available at: http://sigport.org/2541.
Yang Song, Chong Xiao Wang, Wee Peng Tay. (2018). "privacy-aware kalman filter poster." Web.
1. Yang Song, Chong Xiao Wang, Wee Peng Tay. privacy-aware kalman filter poster [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2541

Improved Noise Characterization for Relative Impulse Response Estimation


Relative Impulse Responses (ReIRs) have several applications in speech enhancement, noise suppression and source localization for multi-channel speech processing in reverberant environments. Noise is usually assumed to be white Gaussian during the estimation of the ReIR between two microphones. We show that the noise in this system identification problem is instead dependent upon the microphone measurements and the ReIR itself.

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Authors:
Bhaskar D. Rao, Ritwik Giri, Tao Zhang
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12 April 2018 - 4:38pm
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Poster

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[1] Bhaskar D. Rao, Ritwik Giri, Tao Zhang, "Improved Noise Characterization for Relative Impulse Response Estimation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2497. Accessed: Jul. 16, 2019.
@article{2497-18,
url = {http://sigport.org/2497},
author = {Bhaskar D. Rao; Ritwik Giri; Tao Zhang },
publisher = {IEEE SigPort},
title = {Improved Noise Characterization for Relative Impulse Response Estimation},
year = {2018} }
TY - EJOUR
T1 - Improved Noise Characterization for Relative Impulse Response Estimation
AU - Bhaskar D. Rao; Ritwik Giri; Tao Zhang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2497
ER -
Bhaskar D. Rao, Ritwik Giri, Tao Zhang. (2018). Improved Noise Characterization for Relative Impulse Response Estimation. IEEE SigPort. http://sigport.org/2497
Bhaskar D. Rao, Ritwik Giri, Tao Zhang, 2018. Improved Noise Characterization for Relative Impulse Response Estimation. Available at: http://sigport.org/2497.
Bhaskar D. Rao, Ritwik Giri, Tao Zhang. (2018). "Improved Noise Characterization for Relative Impulse Response Estimation." Web.
1. Bhaskar D. Rao, Ritwik Giri, Tao Zhang. Improved Noise Characterization for Relative Impulse Response Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2497

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