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ICASSP 2020

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2020 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit website.

Variable Projection for Multiple Frequency Estimation


The estimation of the frequencies of multiple complex sinusoids in the presence of noise is required in many applications such as sonar, speech processing, communications, and power systems. This problem can be reformulated as a separable nonlinear least squares problem (SNLLS). In this paper, such formulation is derived and a variable projection (VP) optimization is proposed for solving the SNLLS problem and estimate the frequency parameters. We also apply a lethargy type theorem for quantifying the difficulty of the optimization.

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Authors:
Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer
Submitted On:
6 May 2020 - 3:40pm
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[1] Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer, "Variable Projection for Multiple Frequency Estimation", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5127. Accessed: Aug. 06, 2020.
@article{5127-20,
url = {http://sigport.org/5127},
author = {Yuneisy E. Garcia Guzman; Péter Kovács; Mario Huemer },
publisher = {IEEE SigPort},
title = {Variable Projection for Multiple Frequency Estimation},
year = {2020} }
TY - EJOUR
T1 - Variable Projection for Multiple Frequency Estimation
AU - Yuneisy E. Garcia Guzman; Péter Kovács; Mario Huemer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5127
ER -
Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer. (2020). Variable Projection for Multiple Frequency Estimation. IEEE SigPort. http://sigport.org/5127
Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer, 2020. Variable Projection for Multiple Frequency Estimation. Available at: http://sigport.org/5127.
Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer. (2020). "Variable Projection for Multiple Frequency Estimation." Web.
1. Yuneisy E. Garcia Guzman, Péter Kovács, Mario Huemer. Variable Projection for Multiple Frequency Estimation [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5127

Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective


The availability and quality of channel state information heavily influences the performance of wireless communication systems. For perfect channel knowledge, optimal signal processing and coding schemes are well studied and often closed-form solutions are known. On the other hand, the case of imperfect channel information is much less understood and closed-form solutions remain unknown in general.

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Authors:
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor
Submitted On:
6 May 2020 - 5:25am
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[1] Holger Boche, Rafael F. Schaefer, and H. Vincent Poor, "Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5126. Accessed: Aug. 06, 2020.
@article{5126-20,
url = {http://sigport.org/5126},
author = {Holger Boche; Rafael F. Schaefer; and H. Vincent Poor },
publisher = {IEEE SigPort},
title = {Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective},
year = {2020} }
TY - EJOUR
T1 - Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective
AU - Holger Boche; Rafael F. Schaefer; and H. Vincent Poor
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5126
ER -
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. (2020). Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective. IEEE SigPort. http://sigport.org/5126
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor, 2020. Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective. Available at: http://sigport.org/5126.
Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. (2020). "Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective." Web.
1. Holger Boche, Rafael F. Schaefer, and H. Vincent Poor. Robust Transmission over Channels with Channel Uncertainty: An Algorithmic Perspective [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5126

Small energy masking for improved neural network training for end-to-end speech recognition


In this paper, we present a Small Energy Masking (SEM) algorithm, which masks inputs having values below a certain threshold. More specifically, a time-frequency bin is masked if the filterbank energy in this bin is less than a certain energy threshold. A uniform distribution is employed to randomly generate the ratio of this energy threshold to the peak filterbank energy of each utterance in decibels. The unmasked feature elements are scaled so that the total sum of the feature values remain the same through this masking procedure.

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Authors:
Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi
Submitted On:
5 May 2020 - 5:27pm
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[1] Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi, "Small energy masking for improved neural network training for end-to-end speech recognition", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5125. Accessed: Aug. 06, 2020.
@article{5125-20,
url = {http://sigport.org/5125},
author = {Chanwoo Kim; Kwangyoun Kim; Sathish Reddy Indurthi },
publisher = {IEEE SigPort},
title = {Small energy masking for improved neural network training for end-to-end speech recognition},
year = {2020} }
TY - EJOUR
T1 - Small energy masking for improved neural network training for end-to-end speech recognition
AU - Chanwoo Kim; Kwangyoun Kim; Sathish Reddy Indurthi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5125
ER -
Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi. (2020). Small energy masking for improved neural network training for end-to-end speech recognition. IEEE SigPort. http://sigport.org/5125
Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi, 2020. Small energy masking for improved neural network training for end-to-end speech recognition. Available at: http://sigport.org/5125.
Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi. (2020). "Small energy masking for improved neural network training for end-to-end speech recognition." Web.
1. Chanwoo Kim, Kwangyoun Kim, Sathish Reddy Indurthi. Small energy masking for improved neural network training for end-to-end speech recognition [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5125

Low-complexity and Reliable Transforms for Physical Unclonable Functions


Noisy measurements of a physical unclonable function (PUF) are used to store secret keys with reliability, security, privacy, and complexity constraints. A new set of low-complexity and orthogonal transforms with no multiplication is proposed to obtain bit-error probability results significantly better than all methods previously proposed for key binding with PUFs. The uniqueness and security performance of a transform selected from the proposed set is shown to be close to optimal.

Paper Details

Authors:
Onur Günlü and Rafael F. Schaefer
Submitted On:
5 May 2020 - 11:06am
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[1] Onur Günlü and Rafael F. Schaefer, "Low-complexity and Reliable Transforms for Physical Unclonable Functions", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5124. Accessed: Aug. 06, 2020.
@article{5124-20,
url = {http://sigport.org/5124},
author = {Onur Günlü and Rafael F. Schaefer },
publisher = {IEEE SigPort},
title = {Low-complexity and Reliable Transforms for Physical Unclonable Functions},
year = {2020} }
TY - EJOUR
T1 - Low-complexity and Reliable Transforms for Physical Unclonable Functions
AU - Onur Günlü and Rafael F. Schaefer
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5124
ER -
Onur Günlü and Rafael F. Schaefer. (2020). Low-complexity and Reliable Transforms for Physical Unclonable Functions. IEEE SigPort. http://sigport.org/5124
Onur Günlü and Rafael F. Schaefer, 2020. Low-complexity and Reliable Transforms for Physical Unclonable Functions. Available at: http://sigport.org/5124.
Onur Günlü and Rafael F. Schaefer. (2020). "Low-complexity and Reliable Transforms for Physical Unclonable Functions." Web.
1. Onur Günlü and Rafael F. Schaefer. Low-complexity and Reliable Transforms for Physical Unclonable Functions [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5124

Robust speaker recognition using unsupervised adversarial invariance


In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pre-trained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions.

Paper Details

Authors:
Monisankha Pal, Shrikanth Narayanan
Submitted On:
5 May 2020 - 1:34am
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[1] Monisankha Pal, Shrikanth Narayanan, "Robust speaker recognition using unsupervised adversarial invariance", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5123. Accessed: Aug. 06, 2020.
@article{5123-20,
url = {http://sigport.org/5123},
author = {Monisankha Pal; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Robust speaker recognition using unsupervised adversarial invariance},
year = {2020} }
TY - EJOUR
T1 - Robust speaker recognition using unsupervised adversarial invariance
AU - Monisankha Pal; Shrikanth Narayanan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5123
ER -
Monisankha Pal, Shrikanth Narayanan. (2020). Robust speaker recognition using unsupervised adversarial invariance. IEEE SigPort. http://sigport.org/5123
Monisankha Pal, Shrikanth Narayanan, 2020. Robust speaker recognition using unsupervised adversarial invariance. Available at: http://sigport.org/5123.
Monisankha Pal, Shrikanth Narayanan. (2020). "Robust speaker recognition using unsupervised adversarial invariance." Web.
1. Monisankha Pal, Shrikanth Narayanan. Robust speaker recognition using unsupervised adversarial invariance [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5123

Robust speaker recognition using unsupervised adversarial invariance


In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pre-trained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions.

Paper Details

Authors:
Monisankha Pal, Shrikanth Narayanan
Submitted On:
5 May 2020 - 1:33am
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[1] Monisankha Pal, Shrikanth Narayanan, "Robust speaker recognition using unsupervised adversarial invariance", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5122. Accessed: Aug. 06, 2020.
@article{5122-20,
url = {http://sigport.org/5122},
author = {Monisankha Pal; Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Robust speaker recognition using unsupervised adversarial invariance},
year = {2020} }
TY - EJOUR
T1 - Robust speaker recognition using unsupervised adversarial invariance
AU - Monisankha Pal; Shrikanth Narayanan
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5122
ER -
Monisankha Pal, Shrikanth Narayanan. (2020). Robust speaker recognition using unsupervised adversarial invariance. IEEE SigPort. http://sigport.org/5122
Monisankha Pal, Shrikanth Narayanan, 2020. Robust speaker recognition using unsupervised adversarial invariance. Available at: http://sigport.org/5122.
Monisankha Pal, Shrikanth Narayanan. (2020). "Robust speaker recognition using unsupervised adversarial invariance." Web.
1. Monisankha Pal, Shrikanth Narayanan. Robust speaker recognition using unsupervised adversarial invariance [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5122

A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION


This paper presents a domain adaptation model for sound event detection. A common challenge for sound event detection is how to deal with the mismatch among different datasets. Typically, the performance of a model will decrease if it is tested on a dataset which is different from the one that the model is trained on. To address this problem, based on convolutional recurrent neural networks (CRNNs), we propose an adapted CRNN (A-CRNN) as an unsupervised adversarial domain adaptation model for sound event detection.

Paper Details

Authors:
Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang
Submitted On:
11 May 2020 - 1:21am
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Presentation slides

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[1] Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang, "A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5121. Accessed: Aug. 06, 2020.
@article{5121-20,
url = {http://sigport.org/5121},
author = {Wei Wei; Hongning Zhu; Emmanouil Benetos; Ye Wang },
publisher = {IEEE SigPort},
title = {A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION},
year = {2020} }
TY - EJOUR
T1 - A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION
AU - Wei Wei; Hongning Zhu; Emmanouil Benetos; Ye Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5121
ER -
Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang. (2020). A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION. IEEE SigPort. http://sigport.org/5121
Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang, 2020. A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION. Available at: http://sigport.org/5121.
Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang. (2020). "A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION." Web.
1. Wei Wei, Hongning Zhu, Emmanouil Benetos, Ye Wang. A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5121

Robust Fundamental Frequency Estimation in Coloured Noise


Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaussian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account.

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Authors:
Andreas Jakobsson, Mads Græsbøll Christensen
Submitted On:
8 May 2020 - 12:48pm
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pitch in colored noise

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[1] Andreas Jakobsson, Mads Græsbøll Christensen, "Robust Fundamental Frequency Estimation in Coloured Noise", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5120. Accessed: Aug. 06, 2020.
@article{5120-20,
url = {http://sigport.org/5120},
author = {Andreas Jakobsson; Mads Græsbøll Christensen },
publisher = {IEEE SigPort},
title = {Robust Fundamental Frequency Estimation in Coloured Noise},
year = {2020} }
TY - EJOUR
T1 - Robust Fundamental Frequency Estimation in Coloured Noise
AU - Andreas Jakobsson; Mads Græsbøll Christensen
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5120
ER -
Andreas Jakobsson, Mads Græsbøll Christensen. (2020). Robust Fundamental Frequency Estimation in Coloured Noise. IEEE SigPort. http://sigport.org/5120
Andreas Jakobsson, Mads Græsbøll Christensen, 2020. Robust Fundamental Frequency Estimation in Coloured Noise. Available at: http://sigport.org/5120.
Andreas Jakobsson, Mads Græsbøll Christensen. (2020). "Robust Fundamental Frequency Estimation in Coloured Noise." Web.
1. Andreas Jakobsson, Mads Græsbøll Christensen. Robust Fundamental Frequency Estimation in Coloured Noise [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5120

Feature Affine Projection Algorithms


There is a growing research interest in proposing new techniques to detect and exploit signals/systems sparsity. Recently, the idea of hidden sparsity has been proposed, and it has been shown that, in many cases, sparsity is not explicit, and some tools are required to expose hidden sparsity. In this paper, we propose the Feature Affine Projection (F-AP) algorithm to reveal hidden sparsity in unknown systems. Indeed, first, the hidden sparsity is revealed using the feature matrix, then it is exploited using some sparsity-promoting penalty function.

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Authors:
Hamed Yazdanpanah
Submitted On:
3 May 2020 - 10:10pm
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[1] Hamed Yazdanpanah, "Feature Affine Projection Algorithms", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5119. Accessed: Aug. 06, 2020.
@article{5119-20,
url = {http://sigport.org/5119},
author = {Hamed Yazdanpanah },
publisher = {IEEE SigPort},
title = {Feature Affine Projection Algorithms},
year = {2020} }
TY - EJOUR
T1 - Feature Affine Projection Algorithms
AU - Hamed Yazdanpanah
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5119
ER -
Hamed Yazdanpanah. (2020). Feature Affine Projection Algorithms. IEEE SigPort. http://sigport.org/5119
Hamed Yazdanpanah, 2020. Feature Affine Projection Algorithms. Available at: http://sigport.org/5119.
Hamed Yazdanpanah. (2020). "Feature Affine Projection Algorithms." Web.
1. Hamed Yazdanpanah. Feature Affine Projection Algorithms [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5119

RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES


In this paper, we present an end-to-end deep convolutional neural network operating on multi-channel raw audio data to localize multiple simultaneously active acoustic sources in space. Previously reported end-to-end deep learning based approaches work well in localizing a single source directly from multi-channel raw-audio, but are not easily extendable to localize multiple sources due to the well known permutation problem.

Paper Details

Authors:
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang
Submitted On:
3 May 2020 - 3:51pm
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Raw Waveform based MSL

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[1] Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5118. Accessed: Aug. 06, 2020.
@article{5118-20,
url = {http://sigport.org/5118},
author = {Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang },
publisher = {IEEE SigPort},
title = {RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES},
year = {2020} }
TY - EJOUR
T1 - RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES
AU - Harshavardhan Sundar; Weiran Wang; Ming Sun; Chao Wang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5118
ER -
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. IEEE SigPort. http://sigport.org/5118
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang, 2020. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES. Available at: http://sigport.org/5118.
Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. (2020). "RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES." Web.
1. Harshavardhan Sundar, Weiran Wang, Ming Sun, Chao Wang. RAW WAVEFORM BASED END-TO-END DEEP CONVOLUTIONAL NETWORK FOR SPATIAL LOCALIZATION OF MULTIPLE ACOUSTIC SOURCES [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5118

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