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ICASSP 2019

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The 2019 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

ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES

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Authors:
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang
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12 May 2019 - 4:02pm
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poster_icassp2019.pdf

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[1] Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4470. Accessed: May. 23, 2019.
@article{4470-19,
url = {http://sigport.org/4470},
author = {Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang },
publisher = {IEEE SigPort},
title = {ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES},
year = {2019} }
TY - EJOUR
T1 - ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES
AU - Karim Armanious; Youssef Mecky; Sergios Gatidis; Bin Yang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4470
ER -
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. IEEE SigPort. http://sigport.org/4470
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang, 2019. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES. Available at: http://sigport.org/4470.
Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. (2019). "ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES." Web.
1. Karim Armanious, Youssef Mecky, Sergios Gatidis, Bin Yang. ADVERSARIAL INPAINTING OF MEDICAL IMAGE MODALITIES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4470

TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION


We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We solve this problem by projecting the signal onto the KS subspace, which is a Kronecker product of different subspaces corresponding to each signal dimension. Under this framework, we define the KS subspaces and the orthogonal projection of the signal onto the KS subspace.

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Authors:
Ishan Jindal, Matthew Nokleby
Submitted On:
12 May 2019 - 1:39pm
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Poster for the paper titled TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION

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[1] Ishan Jindal, Matthew Nokleby, "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4468. Accessed: May. 23, 2019.
@article{4468-19,
url = {http://sigport.org/4468},
author = {Ishan Jindal; Matthew Nokleby },
publisher = {IEEE SigPort},
title = {TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION},
year = {2019} }
TY - EJOUR
T1 - TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION
AU - Ishan Jindal; Matthew Nokleby
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4468
ER -
Ishan Jindal, Matthew Nokleby. (2019). TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. IEEE SigPort. http://sigport.org/4468
Ishan Jindal, Matthew Nokleby, 2019. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION. Available at: http://sigport.org/4468.
Ishan Jindal, Matthew Nokleby. (2019). "TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION." Web.
1. Ishan Jindal, Matthew Nokleby. TENSOR MATCHED KRONECKER-STRUCTURED SUBSPACE DETECTION FOR MISSING INFORMATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4468

FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES


Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery. In this paper, we propose an algorithm for signal reconstruction from compressed measurements with image priors captured by a generative model. We search and constrain on latent variable space to make the method stable when the number of compressed measurements is extremely limited. We show that, by exploiting certain structures of the latent variables, the proposed method produces improved reconstruction accuracy and preserves realistic and non-smooth features in the image.

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12 May 2019 - 12:59pm
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Xu, Shaojie ICCASP 2019 Presentation Slides.pdf

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[1] , "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4467. Accessed: May. 23, 2019.
@article{4467-19,
url = {http://sigport.org/4467},
author = { },
publisher = {IEEE SigPort},
title = {FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES},
year = {2019} }
TY - EJOUR
T1 - FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4467
ER -
. (2019). FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. IEEE SigPort. http://sigport.org/4467
, 2019. FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES. Available at: http://sigport.org/4467.
. (2019). "FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES." Web.
1. . FAST COMPRESSIVE SENSING RECOVERY USING GENERATIVE MODELS WITH STRUCTURED LATENT VARIABLES [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4467

ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION


Fluent and confident speech is desirable to every speaker. But professional speech delivering requires a great deal of experience and practice. In this paper, we propose a speech stream manipulation system which can help non-professional speakers to produce fluent, professional-like speech content, in turn contributing towards better listener engagement and comprehension. We propose to achieve this task by manipulating the disfluencies in human speech, like the sounds uh and um, the filler words and awkward long silences.

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Authors:
Nisha Gandhi, Tejas Naik, Roy Shilkrot
Submitted On:
12 May 2019 - 12:38pm
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poster_v2.0.pdf

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[1] Nisha Gandhi, Tejas Naik, Roy Shilkrot, "ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4466. Accessed: May. 23, 2019.
@article{4466-19,
url = {http://sigport.org/4466},
author = {Nisha Gandhi; Tejas Naik; Roy Shilkrot },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION
AU - Nisha Gandhi; Tejas Naik; Roy Shilkrot
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4466
ER -
Nisha Gandhi, Tejas Naik, Roy Shilkrot. (2019). ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION. IEEE SigPort. http://sigport.org/4466
Nisha Gandhi, Tejas Naik, Roy Shilkrot, 2019. ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION. Available at: http://sigport.org/4466.
Nisha Gandhi, Tejas Naik, Roy Shilkrot. (2019). "ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION." Web.
1. Nisha Gandhi, Tejas Naik, Roy Shilkrot. ICASSP 2019 Paper #4001: INCREASE APPARENT PUBLIC SPEAKING FLUENCY BY SPEECH AUGMENTATION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4466

Speaker Diarisation Using 2D Self-attentive Combination of Embeddings


Speaker diarisation systems often cluster audio segments using speaker embeddings such as i-vectors and d-vectors. Since different types of embeddings are often complementary, this paper proposes a generic framework to improve performance by combining them into a single embedding, referred to as a c-vector. This combination uses a 2-dimensional (2D) self-attentive structure, which extends the standard self-attentive layer by averaging not only across time but also across different types of embeddings.

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Authors:
Guangzhi Sun, Chao Zhang, Phil Woodland
Submitted On:
12 May 2019 - 11:10am
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DiarisationPresentation3.pdf

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[1] Guangzhi Sun, Chao Zhang, Phil Woodland, "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4465. Accessed: May. 23, 2019.
@article{4465-19,
url = {http://sigport.org/4465},
author = {Guangzhi Sun; Chao Zhang; Phil Woodland },
publisher = {IEEE SigPort},
title = {Speaker Diarisation Using 2D Self-attentive Combination of Embeddings},
year = {2019} }
TY - EJOUR
T1 - Speaker Diarisation Using 2D Self-attentive Combination of Embeddings
AU - Guangzhi Sun; Chao Zhang; Phil Woodland
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4465
ER -
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. IEEE SigPort. http://sigport.org/4465
Guangzhi Sun, Chao Zhang, Phil Woodland, 2019. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings. Available at: http://sigport.org/4465.
Guangzhi Sun, Chao Zhang, Phil Woodland. (2019). "Speaker Diarisation Using 2D Self-attentive Combination of Embeddings." Web.
1. Guangzhi Sun, Chao Zhang, Phil Woodland. Speaker Diarisation Using 2D Self-attentive Combination of Embeddings [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4465

Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection


In this paper, we address the problem of bird audio detec-
tion and propose a new convolutional neural network archi-
tecture together with a divergence based information channel
weighing strategy in order to achieve improved state-of-the-
art performance and faster convergence. The effectiveness of
the methodology is shown on the Bird Audio Detection Chal-
lenge 2018 (Detection and Classification of Acoustic Scenes
and Events Challenge, Task 3) development data set.

Paper Details

Authors:
Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos
Submitted On:
12 May 2019 - 9:54am
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https://ieeexplore.ieee.org/document/8682483

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[1] Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos, "Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4464. Accessed: May. 23, 2019.
@article{4464-19,
url = {http://sigport.org/4464},
author = {Cemre Zor; Muhammad Awais; Josef Kittler; Miroslaw Bober; Sameed Husain; Qiuqiang Kong; Christian Kroos },
publisher = {IEEE SigPort},
title = {Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection},
year = {2019} }
TY - EJOUR
T1 - Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection
AU - Cemre Zor; Muhammad Awais; Josef Kittler; Miroslaw Bober; Sameed Husain; Qiuqiang Kong; Christian Kroos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4464
ER -
Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos. (2019). Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection. IEEE SigPort. http://sigport.org/4464
Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos, 2019. Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection. Available at: http://sigport.org/4464.
Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos. (2019). "Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection." Web.
1. Cemre Zor, Muhammad Awais, Josef Kittler, Miroslaw Bober, Sameed Husain, Qiuqiang Kong, Christian Kroos. Divergence Based Weighting for Information Channels in Deep Convolutional Neural Networks for Bird Audio Detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4464

Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks


We introduce a method to improve the quality of simple scalar quantization in the context of acoustic sensor networks by combining ideas from sparse reconstruction, artificial neural networks and weighting filters. We start from the observation that optimization methods based on sparse reconstruction resemble the structure of a neural network. Hence, building upon a successful enhancement method, we unroll the algorithms and use this to build a neural network which we train to obtain enhanced decoding.

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Authors:
Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt
Submitted On:
12 May 2019 - 6:42pm
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[1] Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt, "Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4462. Accessed: May. 23, 2019.
@article{4462-19,
url = {http://sigport.org/4462},
author = {Ziyue Zhao; Dirk Lorenz; Tim Fingscheidt },
publisher = {IEEE SigPort},
title = {Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks},
year = {2019} }
TY - EJOUR
T1 - Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks
AU - Ziyue Zhao; Dirk Lorenz; Tim Fingscheidt
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4462
ER -
Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt. (2019). Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks. IEEE SigPort. http://sigport.org/4462
Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt, 2019. Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks. Available at: http://sigport.org/4462.
Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt. (2019). "Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks." Web.
1. Ziyue Zhao, Dirk Lorenz, Tim Fingscheidt. Learning to Dequantize Speech Signals by Primal-Dual Networks: An Approach for Acoustic Sensor Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4462

MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING

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Authors:
Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos
Submitted On:
12 May 2019 - 5:16am
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ICASSP_2019_v4.pdf

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[1] Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos, "MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4461. Accessed: May. 23, 2019.
@article{4461-19,
url = {http://sigport.org/4461},
author = {Pablo Ruiz; Oliver Cossairt; Aggelos Katsaggelos },
publisher = {IEEE SigPort},
title = {MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING},
year = {2019} }
TY - EJOUR
T1 - MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING
AU - Pablo Ruiz; Oliver Cossairt; Aggelos Katsaggelos
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4461
ER -
Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos. (2019). MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING. IEEE SigPort. http://sigport.org/4461
Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos, 2019. MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING. Available at: http://sigport.org/4461.
Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos. (2019). "MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING." Web.
1. Pablo Ruiz, Oliver Cossairt, Aggelos Katsaggelos. MULTI-FRAME SUPER-RESOLUTION FOR TIME-OF-FLIGHT IMAGING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4461

A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM

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12 May 2019 - 4:31am
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ICASSP2019_poster_deepMTT.pdf

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[1] , "A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4459. Accessed: May. 23, 2019.
@article{4459-19,
url = {http://sigport.org/4459},
author = { },
publisher = {IEEE SigPort},
title = {A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM},
year = {2019} }
TY - EJOUR
T1 - A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4459
ER -
. (2019). A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM. IEEE SigPort. http://sigport.org/4459
, 2019. A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM. Available at: http://sigport.org/4459.
. (2019). "A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM." Web.
1. . A DEEP NEURAL NETWORK BASED MANEUVERING-TARGET TRACKING ALGORITHM [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4459

Universal Acoustic Using Neural Mixture Models

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Authors:
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu
Submitted On:
12 May 2019 - 2:42am
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UAM_v3.pdf

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[1] Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, "Universal Acoustic Using Neural Mixture Models", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4458. Accessed: May. 23, 2019.
@article{4458-19,
url = {http://sigport.org/4458},
author = {Amit Das; Jinyu Li; Yifan Gong; Changliang Lu },
publisher = {IEEE SigPort},
title = {Universal Acoustic Using Neural Mixture Models},
year = {2019} }
TY - EJOUR
T1 - Universal Acoustic Using Neural Mixture Models
AU - Amit Das; Jinyu Li; Yifan Gong; Changliang Lu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4458
ER -
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). Universal Acoustic Using Neural Mixture Models. IEEE SigPort. http://sigport.org/4458
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu, 2019. Universal Acoustic Using Neural Mixture Models. Available at: http://sigport.org/4458.
Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. (2019). "Universal Acoustic Using Neural Mixture Models." Web.
1. Amit Das, Jinyu Li, Yifan Gong, Changliang Lu. Universal Acoustic Using Neural Mixture Models [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4458

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