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Audio and Acoustic Signal Processing

ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING

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10 May 2019 - 7:33am
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[1] , "ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4305. Accessed: Oct. 17, 2019.
@article{4305-19,
url = {http://sigport.org/4305},
author = { },
publisher = {IEEE SigPort},
title = {ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING},
year = {2019} }
TY - EJOUR
T1 - ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4305
ER -
. (2019). ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING. IEEE SigPort. http://sigport.org/4305
, 2019. ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING. Available at: http://sigport.org/4305.
. (2019). "ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING." Web.
1. . ITERATIVELYREWEIGHTEDPENALTYALTERNATINGMINIMIZATIONMETHODS WITHCONTINUATIONFORIMAGEDEBLURRING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4305

Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals


Continuously-worn wearable sensors produce copious amounts of rich bio-behavioral time series recordings. Exploring recurring patterns, often known as motifs, in wearable time series offers critical insights into understanding the nature of human behavior. Challenges in discovering motifs from wearable recordings include noise removal, pattern generalization, and accounting for subtle variations between subsequences in one motif set.

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Authors:
Shrikanth Narayanan
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10 May 2019 - 6:20am
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[1] Shrikanth Narayanan, "Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4297. Accessed: Oct. 17, 2019.
@article{4297-19,
url = {http://sigport.org/4297},
author = {Shrikanth Narayanan },
publisher = {IEEE SigPort},
title = {Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals},
year = {2019} }
TY - EJOUR
T1 - Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals
AU - Shrikanth Narayanan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4297
ER -
Shrikanth Narayanan. (2019). Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals. IEEE SigPort. http://sigport.org/4297
Shrikanth Narayanan, 2019. Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals. Available at: http://sigport.org/4297.
Shrikanth Narayanan. (2019). "Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals." Web.
1. Shrikanth Narayanan. Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4297

Perceptually-motivated environment-specific speech enhancement


This paper introduces a deep learning approach to enhance speech recordings made in a specific environment. A single neural network learns to ameliorate several types of recording artifacts, including noise, reverberation, and non-linear equalization. The method relies on a new perceptual loss function that combines adversarial loss with spectrogram features. Both subjective and objective evaluations show that the proposed approach improves on state-of-the-art baseline methods.

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Authors:
Jiaqi Su, Adam Finkelstein, Zeyu Jin
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10 May 2019 - 1:40am
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[1] Jiaqi Su, Adam Finkelstein, Zeyu Jin, "Perceptually-motivated environment-specific speech enhancement", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4272. Accessed: Oct. 17, 2019.
@article{4272-19,
url = {http://sigport.org/4272},
author = {Jiaqi Su; Adam Finkelstein; Zeyu Jin },
publisher = {IEEE SigPort},
title = {Perceptually-motivated environment-specific speech enhancement},
year = {2019} }
TY - EJOUR
T1 - Perceptually-motivated environment-specific speech enhancement
AU - Jiaqi Su; Adam Finkelstein; Zeyu Jin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4272
ER -
Jiaqi Su, Adam Finkelstein, Zeyu Jin. (2019). Perceptually-motivated environment-specific speech enhancement. IEEE SigPort. http://sigport.org/4272
Jiaqi Su, Adam Finkelstein, Zeyu Jin, 2019. Perceptually-motivated environment-specific speech enhancement. Available at: http://sigport.org/4272.
Jiaqi Su, Adam Finkelstein, Zeyu Jin. (2019). "Perceptually-motivated environment-specific speech enhancement." Web.
1. Jiaqi Su, Adam Finkelstein, Zeyu Jin. Perceptually-motivated environment-specific speech enhancement [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4272

SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS


Dictionary learning algorithms have been successfully applied to a number of signal and image processing problems. In some applications however, the observed signals may have a multi-subpsace structure that enables block-sparse signal representations. Based on the observation that the observed signals can be approximated as a sum of low rank matrices, a new algorithm for learning a block-structured dictionary for block-sparse signal representations is proposed.

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Authors:
Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim
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10 May 2019 - 1:47am
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[1] Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim, "SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4267. Accessed: Oct. 17, 2019.
@article{4267-19,
url = {http://sigport.org/4267},
author = {Abd-Krim Seghouane; Asif Iqbal; Karim Abed-Meraim },
publisher = {IEEE SigPort},
title = {SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS},
year = {2019} }
TY - EJOUR
T1 - SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS
AU - Abd-Krim Seghouane; Asif Iqbal; Karim Abed-Meraim
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4267
ER -
Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim. (2019). SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS. IEEE SigPort. http://sigport.org/4267
Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim, 2019. SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS. Available at: http://sigport.org/4267.
Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim. (2019). "SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS." Web.
1. Abd-Krim Seghouane, Asif Iqbal, Karim Abed-Meraim. SEQUENTIAL STRUCTURED DICTIONARY LEARNING FOR BLOCK SPARSE REPRESENTATIONS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4267

DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS


CPS comprised of ordinary people or first responders is proposed to detect gas vapor in open air.
This CPS will use low-cost sensors coupled to smart phones or mobile devices.
The efficacy of CPS hinges on its ability to address technical challenges stemming from the fact that sensors may produce different results under the same conditions due to sensor drift, noise, and/or resolution errors.
The proposed system makes use of time-varying signals produced by sensors to detect gas leaks. Sensors sample the gas vapor level in a continuous manner

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Authors:
Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin
Submitted On:
9 May 2019 - 11:17pm
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[1] Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin, "DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4262. Accessed: Oct. 17, 2019.
@article{4262-19,
url = {http://sigport.org/4262},
author = {Diaa Badawi; Sule Ozev; Jennifer Blain Christen; Chengmo Yang; Alex Orailoglu; Ahmet Enis Cetin },
publisher = {IEEE SigPort},
title = {DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS},
year = {2019} }
TY - EJOUR
T1 - DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS
AU - Diaa Badawi; Sule Ozev; Jennifer Blain Christen; Chengmo Yang; Alex Orailoglu; Ahmet Enis Cetin
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4262
ER -
Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin. (2019). DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS. IEEE SigPort. http://sigport.org/4262
Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin, 2019. DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS. Available at: http://sigport.org/4262.
Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin. (2019). "DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS." Web.
1. Diaa Badawi, Sule Ozev, Jennifer Blain Christen, Chengmo Yang, Alex Orailoglu, Ahmet Enis Cetin. DETECTING GAS VAPOR LEAKS THROUGH UNCALIBRATED SENSOR BASED CPS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4262

BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS


The objective of the study is to develop a framework for automatic breast cancer detection with merging four imaging modes. Attempts were made for tumor classification and segmentation; using a multi-parametric Magnetic Resonance Imaging (MRI) method on breast tumors. MRI data of the breast were obtained from 67 subjects with a 1.5T-MRI scanner. Four imaging modes: were T1 weighted, T2 weighted, Diffusion Weighted and eTHRIVE sequences, and dynamic- contrast-enhanced(DCE)-MRI parameters are acquired.

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Authors:
Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei
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9 May 2019 - 10:56pm
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[1] Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei, "BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4255. Accessed: Oct. 17, 2019.
@article{4255-19,
url = {http://sigport.org/4255},
author = {Wenhuan Lu; Zhe Wang; Yuqing He; Hong Yu; Naixue Xiong; Jianguo Wei },
publisher = {IEEE SigPort},
title = {BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS},
year = {2019} }
TY - EJOUR
T1 - BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS
AU - Wenhuan Lu; Zhe Wang; Yuqing He; Hong Yu; Naixue Xiong; Jianguo Wei
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4255
ER -
Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei. (2019). BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS. IEEE SigPort. http://sigport.org/4255
Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei, 2019. BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS. Available at: http://sigport.org/4255.
Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei. (2019). "BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS." Web.
1. Wenhuan Lu, Zhe Wang, Yuqing He, Hong Yu, Naixue Xiong, Jianguo Wei. BREAST CANCER DETECTION BASED ON MERGING FOUR MODES MRI USING CONVOLUTIONAL NEURAL NETWORKS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4255

Modality attention for end-to-end audio-visual speech recognition


Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for robust speech recognition, especially in noisy environment. In this paper, we propose a novel multimodal attention based method for audio-visual speech recognition which could automatically learn the fused representation from both modalities based on their importance. Our method is realized using state-of-the-art sequence-to-sequence (Seq2seq) architectures.

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Authors:
Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia
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9 May 2019 - 12:27pm
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[1] Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia, "Modality attention for end-to-end audio-visual speech recognition", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4218. Accessed: Oct. 17, 2019.
@article{4218-19,
url = {http://sigport.org/4218},
author = {Wenwen Yang; Wei Chen; Yanfeng Wang; Jia Jia },
publisher = {IEEE SigPort},
title = {Modality attention for end-to-end audio-visual speech recognition},
year = {2019} }
TY - EJOUR
T1 - Modality attention for end-to-end audio-visual speech recognition
AU - Wenwen Yang; Wei Chen; Yanfeng Wang; Jia Jia
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4218
ER -
Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia. (2019). Modality attention for end-to-end audio-visual speech recognition. IEEE SigPort. http://sigport.org/4218
Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia, 2019. Modality attention for end-to-end audio-visual speech recognition. Available at: http://sigport.org/4218.
Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia. (2019). "Modality attention for end-to-end audio-visual speech recognition." Web.
1. Wenwen Yang, Wei Chen, Yanfeng Wang, Jia Jia. Modality attention for end-to-end audio-visual speech recognition [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4218

Anomaly Detection in Raw Audio Using Deep Autoregressive Networks


Anomaly detection involves the recognition of patterns outside of what is considered normal, given a certain set of input data. This presents a unique set of challenges for machine learning, particularly if we assume a semi-supervised scenario in which anomalous patterns are unavailable at training time meaning algorithms must rely on non-anomalous data alone. Anomaly detection in time series adds an additional level of complexity given the contextual nature of anomalies.

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Authors:
Ellen Rushe, Brian Mac Namee
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9 May 2019 - 11:12am
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[1] Ellen Rushe, Brian Mac Namee, "Anomaly Detection in Raw Audio Using Deep Autoregressive Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4211. Accessed: Oct. 17, 2019.
@article{4211-19,
url = {http://sigport.org/4211},
author = {Ellen Rushe; Brian Mac Namee },
publisher = {IEEE SigPort},
title = {Anomaly Detection in Raw Audio Using Deep Autoregressive Networks},
year = {2019} }
TY - EJOUR
T1 - Anomaly Detection in Raw Audio Using Deep Autoregressive Networks
AU - Ellen Rushe; Brian Mac Namee
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4211
ER -
Ellen Rushe, Brian Mac Namee. (2019). Anomaly Detection in Raw Audio Using Deep Autoregressive Networks. IEEE SigPort. http://sigport.org/4211
Ellen Rushe, Brian Mac Namee, 2019. Anomaly Detection in Raw Audio Using Deep Autoregressive Networks. Available at: http://sigport.org/4211.
Ellen Rushe, Brian Mac Namee. (2019). "Anomaly Detection in Raw Audio Using Deep Autoregressive Networks." Web.
1. Ellen Rushe, Brian Mac Namee. Anomaly Detection in Raw Audio Using Deep Autoregressive Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4211

Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival


In this paper we study the problem of estimating receiver and sender positions from time-difference-of-arrival measurements, assuming an unknown constant time-difference-of- arrival offset. This problem is relevant for example for repetitive sound events. In this paper it is shown that there are three minimal cases to the problem. One of these (the five receiver, five sender problem) is of particular importance. A fast solver (with run-time under 4 μs) is given.

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Authors:
Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström
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9 May 2019 - 10:39am
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[1] Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström, "Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4208. Accessed: Oct. 17, 2019.
@article{4208-19,
url = {http://sigport.org/4208},
author = {Gabrielle Flood; Thejasvi Beleyur; Viktor Larsson; Holger R. Goerlitz; Magnus Oskarsson; Kalle Åström },
publisher = {IEEE SigPort},
title = {Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival},
year = {2019} }
TY - EJOUR
T1 - Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival
AU - Gabrielle Flood; Thejasvi Beleyur; Viktor Larsson; Holger R. Goerlitz; Magnus Oskarsson; Kalle Åström
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4208
ER -
Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström. (2019). Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival. IEEE SigPort. http://sigport.org/4208
Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström, 2019. Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival. Available at: http://sigport.org/4208.
Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström. (2019). "Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival." Web.
1. Gabrielle Flood, Thejasvi Beleyur, Viktor Larsson, Holger R. Goerlitz, Magnus Oskarsson, Kalle Åström. Robust Self-Calibration of Constant Offset Time-Difference-of-Arrival [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4208

Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes

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Authors:
Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller
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10 May 2019 - 6:08am
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[1] Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller, "Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4204. Accessed: Oct. 17, 2019.
@article{4204-19,
url = {http://sigport.org/4204},
author = {Zhao Ren; Qiuqiang Kong; Jing Han; Mark D. Plumbley; Björn W. Schuller },
publisher = {IEEE SigPort},
title = {Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes},
year = {2019} }
TY - EJOUR
T1 - Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes
AU - Zhao Ren; Qiuqiang Kong; Jing Han; Mark D. Plumbley; Björn W. Schuller
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4204
ER -
Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller. (2019). Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes. IEEE SigPort. http://sigport.org/4204
Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller, 2019. Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes. Available at: http://sigport.org/4204.
Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller. (2019). "Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes." Web.
1. Zhao Ren, Qiuqiang Kong, Jing Han, Mark D. Plumbley, Björn W. Schuller. Attention-based Atrous Convolutional Neural Networks: Visualisation and Understanding Perspectives of Acoustic Scenes [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4204

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