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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.

BUT System for the Second DIHARD Speech Diarization Challenge


This paper describes the winning systems developed by the BUT team for the four tracks of the Second DIHARD Speech Diarization Challenge. For tracks 1 and 2 the systems were mainly based on performing agglomerative hierarchical clustering (AHC) of x-vectors, followed by another x-vector clustering based on Bayes hidden Markov model and variational Bayes inference. We provide a comparison of the improvement given by each step and share the implementation of the core of the system.

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Authors:
Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin
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15 May 2020 - 3:46am
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BUT_System_for_the_Second_DIHARD_Speech_Diarization_Challenge.pdf

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[1] Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin, "BUT System for the Second DIHARD Speech Diarization Challenge", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5341. Accessed: Jul. 15, 2020.
@article{5341-20,
url = {http://sigport.org/5341},
author = {Federico Landini; Shuai Wang; Mireia Diez; Lukáš Burget; Pavel Matějka; Kateřina Žmolíková; Ladislav Mošner; Anna Silnova; Oldřich Plchot; Ondřej Novotný; Hossein Zeinali; Johan Rodhin },
publisher = {IEEE SigPort},
title = {BUT System for the Second DIHARD Speech Diarization Challenge},
year = {2020} }
TY - EJOUR
T1 - BUT System for the Second DIHARD Speech Diarization Challenge
AU - Federico Landini; Shuai Wang; Mireia Diez; Lukáš Burget; Pavel Matějka; Kateřina Žmolíková; Ladislav Mošner; Anna Silnova; Oldřich Plchot; Ondřej Novotný; Hossein Zeinali; Johan Rodhin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5341
ER -
Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin. (2020). BUT System for the Second DIHARD Speech Diarization Challenge. IEEE SigPort. http://sigport.org/5341
Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin, 2020. BUT System for the Second DIHARD Speech Diarization Challenge. Available at: http://sigport.org/5341.
Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin. (2020). "BUT System for the Second DIHARD Speech Diarization Challenge." Web.
1. Federico Landini, Shuai Wang, Mireia Diez, Lukáš Burget, Pavel Matějka, Kateřina Žmolíková, Ladislav Mošner, Anna Silnova, Oldřich Plchot, Ondřej Novotný, Hossein Zeinali, Johan Rodhin. BUT System for the Second DIHARD Speech Diarization Challenge [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5341

FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS


While there is now a significant literature on sparse inverse covariance estimation, all that literature, with only a couple of exceptions, has dealt only with univariate (or scalar) net- works where each node carries a univariate signal. However in many, perhaps most, applications, each node may carry multivariate signals representing multi-attribute data, possibly of different dimensions. Modelling such multivariate (or vector) networks requires fitting block-sparse inverse covariance matrices. Here we achieve maximal block sparsity by maximizing a block-l0-sparse penalized likelihood.

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15 May 2020 - 3:00am
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[1] , "FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5340. Accessed: Jul. 15, 2020.
@article{5340-20,
url = {http://sigport.org/5340},
author = { },
publisher = {IEEE SigPort},
title = {FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS},
year = {2020} }
TY - EJOUR
T1 - FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5340
ER -
. (2020). FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS. IEEE SigPort. http://sigport.org/5340
, 2020. FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS. Available at: http://sigport.org/5340.
. (2020). "FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS." Web.
1. . FAST BLOCK-SPARSE ESTIMATION FOR VECTOR NETWORKS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5340

LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs


Time-series clustering involves grouping homogeneous time series together based on certain similarity measures. The mixture AR model (MxAR) has already been developed for time series clustering, as has an associated EM algorithm. How- ever, this EM clustering algorithm fails to perform satisfactorily in large-scale applications due to its high computational complexity. This paper proposes a new algorithm, k-ARs, which is a limiting version of the existing EM algorithm.

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15 May 2020 - 2:55am
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kARs.pdf

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[1] , "LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5339. Accessed: Jul. 15, 2020.
@article{5339-20,
url = {http://sigport.org/5339},
author = { },
publisher = {IEEE SigPort},
title = {LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs},
year = {2020} }
TY - EJOUR
T1 - LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5339
ER -
. (2020). LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs. IEEE SigPort. http://sigport.org/5339
, 2020. LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs. Available at: http://sigport.org/5339.
. (2020). "LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs." Web.
1. . LARGE-SCALE TIME SERIES CLUSTERING WITH k-ARs [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5339

DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS


Sparse code multiple access (SCMA) uses multi-dimensional sparse codewords to transmit user data. The expectation propagation algorithm (EPA) exploiting the sparse property shows linear complexity growth and thus is preferred for multi-user detection. To further reduce the complexity, a convergence-aware based EPA for uplink MIMO SCMA systems is proposed. Techniques including user termination, antenna termination, and codebook reduction are adopted. The user termination must be combined with the iteration constraint to avoid misjudgement.

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Authors:
Jih-Yang Lin, Pei-Yun Tsai
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15 May 2020 - 2:08am
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SCMA Presentation PPT_v2_upload.pdf

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[1] Jih-Yang Lin, Pei-Yun Tsai, "DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5338. Accessed: Jul. 15, 2020.
@article{5338-20,
url = {http://sigport.org/5338},
author = {Jih-Yang Lin; Pei-Yun Tsai },
publisher = {IEEE SigPort},
title = {DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS},
year = {2020} }
TY - EJOUR
T1 - DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS
AU - Jih-Yang Lin; Pei-Yun Tsai
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5338
ER -
Jih-Yang Lin, Pei-Yun Tsai. (2020). DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS. IEEE SigPort. http://sigport.org/5338
Jih-Yang Lin, Pei-Yun Tsai, 2020. DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS. Available at: http://sigport.org/5338.
Jih-Yang Lin, Pei-Yun Tsai. (2020). "DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS." Web.
1. Jih-Yang Lin, Pei-Yun Tsai. DESIGN OF A CONVERGENCE-AWARE BASED EXPECTATION PROPAGATION ALGORITHM FOR UPLINK MIMO SCMA SYSTEMS [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5338

Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses


Speech enhancement has greatly benefited from deep learning. Currently, the best performing deep architectures use long short-term memory (LSTM) recurrent neural networks (RNNs) to model short and long temporal dependencies. These approaches, however, underutilize or ignore spectral-level dependencies within the magnitude and phase responses, respectively. In this paper, we propose a deep learning architecture that leverages both temporal and spectral dependencies within the magnitude and phase responses.

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Authors:
Khandokar Md. Nayem, Donald S. Williamson
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15 May 2020 - 2:02am
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ICASSP 2020 presentation slides on Intra-spectral speech enhancement

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[1] Khandokar Md. Nayem, Donald S. Williamson, "Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5337. Accessed: Jul. 15, 2020.
@article{5337-20,
url = {http://sigport.org/5337},
author = {Khandokar Md. Nayem; Donald S. Williamson },
publisher = {IEEE SigPort},
title = {Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses},
year = {2020} }
TY - EJOUR
T1 - Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses
AU - Khandokar Md. Nayem; Donald S. Williamson
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5337
ER -
Khandokar Md. Nayem, Donald S. Williamson. (2020). Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses. IEEE SigPort. http://sigport.org/5337
Khandokar Md. Nayem, Donald S. Williamson, 2020. Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses. Available at: http://sigport.org/5337.
Khandokar Md. Nayem, Donald S. Williamson. (2020). "Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses." Web.
1. Khandokar Md. Nayem, Donald S. Williamson. Monaural Speech Enhancement Using Intra-Spectral Recurrent Layers In The Magnitude And Phase Responses [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5337

TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION

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Authors:
Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin
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15 May 2020 - 1:40am
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[1] Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin, "TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5336. Accessed: Jul. 15, 2020.
@article{5336-20,
url = {http://sigport.org/5336},
author = {Jie Yan; Yan Song; Li-Rong Dai; Ian McLoughlin },
publisher = {IEEE SigPort},
title = {TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION},
year = {2020} }
TY - EJOUR
T1 - TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION
AU - Jie Yan; Yan Song; Li-Rong Dai; Ian McLoughlin
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5336
ER -
Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin. (2020). TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION. IEEE SigPort. http://sigport.org/5336
Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin, 2020. TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION. Available at: http://sigport.org/5336.
Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin. (2020). "TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION." Web.
1. Jie Yan, Yan Song, Li-Rong Dai, Ian McLoughlin. TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5336

Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction


Latent fingerprint reconstruction is a vital preprocessing step for its identification. This task is very challenging due to not only existing complicated degradation patterns but also its scarcity of paired training data. To address these challenges, we propose a novel generative adversarial network (GAN) based data augmentation scheme to improve such reconstruction.

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Authors:
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang
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15 May 2020 - 1:16am
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ICASSP1263.pdf

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[1] Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5335. Accessed: Jul. 15, 2020.
@article{5335-20,
url = {http://sigport.org/5335},
author = {Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang },
publisher = {IEEE SigPort},
title = {Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction},
year = {2020} }
TY - EJOUR
T1 - Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction
AU - Ying Xu; Yi Wang; Jiajun Liang; Yong Jiang
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5335
ER -
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. IEEE SigPort. http://sigport.org/5335
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang, 2020. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction. Available at: http://sigport.org/5335.
Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. (2020). "Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction." Web.
1. Ying Xu, Yi Wang, Jiajun Liang, Yong Jiang. Augmentation Data Synthesis via GANs: Boosting Latent Fingerprint Reconstruction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5335

Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings


Involvement hot spots have been proposed as a useful concept for meeting analysis and studied off and on for over 15 years. These are regions of meetings that are marked by high participant involvement, as judged by human annotators. However, prior work was either not conducted in a formal machine learning setting, or focused on only a subset of possible meeting features or downstream applications (such as summarization). In this paper we investigate to what extent various acoustic, linguistic and pragmatic aspects of the meetings, both in isolation and jointly, can help detect hot spots.

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Authors:
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke
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15 May 2020 - 1:11am
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[1] Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke, "Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5334. Accessed: Jul. 15, 2020.
@article{5334-20,
url = {http://sigport.org/5334},
author = {Dave Makhervaks; William Hinthorn; Dimitrios Dimitriadis; Andreas Stolcke },
publisher = {IEEE SigPort},
title = {Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings},
year = {2020} }
TY - EJOUR
T1 - Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings
AU - Dave Makhervaks; William Hinthorn; Dimitrios Dimitriadis; Andreas Stolcke
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5334
ER -
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. (2020). Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings. IEEE SigPort. http://sigport.org/5334
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke, 2020. Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings. Available at: http://sigport.org/5334.
Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. (2020). "Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings." Web.
1. Dave Makhervaks, William Hinthorn, Dimitrios Dimitriadis, Andreas Stolcke. Combining Acoustics, Content and Interaction Features to Find Hot Spots in Meetings [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5334

Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering

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Authors:
Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu
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15 May 2020 - 12:14am
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Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering.pdf

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[1] Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu, "Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5333. Accessed: Jul. 15, 2020.
@article{5333-20,
url = {http://sigport.org/5333},
author = {Rui Huang; Jun Tao; Le Yang; Yanbo Xue; Qisong Wu },
publisher = {IEEE SigPort},
title = {Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering},
year = {2020} }
TY - EJOUR
T1 - Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering
AU - Rui Huang; Jun Tao; Le Yang; Yanbo Xue; Qisong Wu
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5333
ER -
Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu. (2020). Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering. IEEE SigPort. http://sigport.org/5333
Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu, 2020. Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering. Available at: http://sigport.org/5333.
Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu. (2020). "Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering." Web.
1. Rui Huang, Jun Tao, Le Yang, Yanbo Xue, Qisong Wu. Robust TDOA Indoor Tracking Using Constrained Measurement Filtering and Grid-Based Filtering [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5333

Deep Multi-Region Hashing

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15 May 2020 - 12:09am
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paper slide: Deep Multi-Region Hashing

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[1] , "Deep Multi-Region Hashing", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5332. Accessed: Jul. 15, 2020.
@article{5332-20,
url = {http://sigport.org/5332},
author = { },
publisher = {IEEE SigPort},
title = {Deep Multi-Region Hashing},
year = {2020} }
TY - EJOUR
T1 - Deep Multi-Region Hashing
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5332
ER -
. (2020). Deep Multi-Region Hashing. IEEE SigPort. http://sigport.org/5332
, 2020. Deep Multi-Region Hashing. Available at: http://sigport.org/5332.
. (2020). "Deep Multi-Region Hashing." Web.
1. . Deep Multi-Region Hashing [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5332

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