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Multimedia Signal Processing

Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement

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Authors:
Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup
Submitted On:
15 September 2017 - 4:29am
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poster_landscape.pdf

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[1] Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup, "Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2108. Accessed: Oct. 20, 2017.
@article{2108-17,
url = {http://sigport.org/2108},
author = {Nils Genser; Jürgen Seiler; Markus Jonscher; André Kaup },
publisher = {IEEE SigPort},
title = {Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement},
year = {2017} }
TY - EJOUR
T1 - Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement
AU - Nils Genser; Jürgen Seiler; Markus Jonscher; André Kaup
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2108
ER -
Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup. (2017). Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement. IEEE SigPort. http://sigport.org/2108
Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup, 2017. Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement. Available at: http://sigport.org/2108.
Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup. (2017). "Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement." Web.
1. Nils Genser, Jürgen Seiler, Markus Jonscher, André Kaup. Demonstration of Rapid Frequency Selective Reconstruction for Image Resolution Enhancement [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2108

Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment

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Authors:
Nils Genser, Jürgen Seiler, André Kaup
Submitted On:
15 September 2017 - 4:30am
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ICIP-2017_Genser.pdf

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[1] Nils Genser, Jürgen Seiler, André Kaup, "Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2106. Accessed: Oct. 20, 2017.
@article{2106-17,
url = {http://sigport.org/2106},
author = {Nils Genser; Jürgen Seiler; André Kaup },
publisher = {IEEE SigPort},
title = {Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment},
year = {2017} }
TY - EJOUR
T1 - Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment
AU - Nils Genser; Jürgen Seiler; André Kaup
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2106
ER -
Nils Genser, Jürgen Seiler, André Kaup. (2017). Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment. IEEE SigPort. http://sigport.org/2106
Nils Genser, Jürgen Seiler, André Kaup, 2017. Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment. Available at: http://sigport.org/2106.
Nils Genser, Jürgen Seiler, André Kaup. (2017). "Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment." Web.
1. Nils Genser, Jürgen Seiler, André Kaup. Scaled Fixed-Point Frequency Selective Extrapolation for Fast Image Error Concealment [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2106

Granularity-Based Interactive Image Display


This paper presents a prototype system that assists users in accessing an unstructured image set (e.g., search results of a query). The system provides a spectrum of overviews, each of which is determined by the display granularity (i.e., the level of summary) an user desires. This new functionality enables a new granularity-based interactive image browsing experience.

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Authors:
Geng-Zhi Wildsky Fann
Submitted On:
11 September 2017 - 3:16am
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icip17_demo.pdf

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[1] Geng-Zhi Wildsky Fann, "Granularity-Based Interactive Image Display", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1895. Accessed: Oct. 20, 2017.
@article{1895-17,
url = {http://sigport.org/1895},
author = {Geng-Zhi Wildsky Fann },
publisher = {IEEE SigPort},
title = {Granularity-Based Interactive Image Display},
year = {2017} }
TY - EJOUR
T1 - Granularity-Based Interactive Image Display
AU - Geng-Zhi Wildsky Fann
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1895
ER -
Geng-Zhi Wildsky Fann. (2017). Granularity-Based Interactive Image Display. IEEE SigPort. http://sigport.org/1895
Geng-Zhi Wildsky Fann, 2017. Granularity-Based Interactive Image Display. Available at: http://sigport.org/1895.
Geng-Zhi Wildsky Fann. (2017). "Granularity-Based Interactive Image Display." Web.
1. Geng-Zhi Wildsky Fann. Granularity-Based Interactive Image Display [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1895

A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK


In this poster, we propose to face the problem of event detection from single images, by exploiting both background information often containing revealing contextual clues and details, which are salient for recognizing the event. Such details are visual objects critical to understand the underlying event depicted in the image and were recently defined in the literature as ”event-saliency”. Adopting the Multiple-Instance Learning (MIL) paradigm we propose a hierarchical approach analyzing first the entire picture and then refining the decision on the basis of the event-salient objects.

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Authors:
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani
Submitted On:
7 December 2016 - 10:30am
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GlobalSIP - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK.pdf

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[1] Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani, "A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1409. Accessed: Oct. 20, 2017.
@article{1409-16,
url = {http://sigport.org/1409},
author = {Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani },
publisher = {IEEE SigPort},
title = {A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK},
year = {2016} }
TY - EJOUR
T1 - A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK
AU - Kashif Ahmad; Francesco De Natale; Giulia Boato; Andrea Rosani
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1409
ER -
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. (2016). A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK. IEEE SigPort. http://sigport.org/1409
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani, 2016. A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK. Available at: http://sigport.org/1409.
Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. (2016). "A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK." Web.
1. Kashif Ahmad, Francesco De Natale, Giulia Boato, Andrea Rosani. A HIERARCHICAL APPROACH TO EVENT DISCOVERY FROM SINGLE IMAGES USING MIL FRAMEWORK [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1409

A Hierarchical Approach to Event Discovery from Single Images using MIL Framework


In this poster, we propose to face the problem of event detec/on from single
images, by exploi/ng both background informaAon oNen containing revealing
contextual clues and details, which are salient for recognizing the event. Such
details are visual objects criAcal to understand the underlying event depicted in
the image and were recently defined in the literature as ”event-saliency”.
Adop/ng the MulAple-Instance Learning (MIL) paradigm we propose a
hierarchical approach analyzing first the en/re picture and then refining the

Paper Details

Authors:
F. G. De Natale, G. Baoto, A. Rosani
Submitted On:
12 September 2017 - 11:01am
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2016-globalSIP_version2.pdf

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[1] F. G. De Natale, G. Baoto, A. Rosani, "A Hierarchical Approach to Event Discovery from Single Images using MIL Framework", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1362. Accessed: Oct. 20, 2017.
@article{1362-16,
url = {http://sigport.org/1362},
author = {F. G. De Natale; G. Baoto; A. Rosani },
publisher = {IEEE SigPort},
title = {A Hierarchical Approach to Event Discovery from Single Images using MIL Framework},
year = {2016} }
TY - EJOUR
T1 - A Hierarchical Approach to Event Discovery from Single Images using MIL Framework
AU - F. G. De Natale; G. Baoto; A. Rosani
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1362
ER -
F. G. De Natale, G. Baoto, A. Rosani. (2016). A Hierarchical Approach to Event Discovery from Single Images using MIL Framework. IEEE SigPort. http://sigport.org/1362
F. G. De Natale, G. Baoto, A. Rosani, 2016. A Hierarchical Approach to Event Discovery from Single Images using MIL Framework. Available at: http://sigport.org/1362.
F. G. De Natale, G. Baoto, A. Rosani. (2016). "A Hierarchical Approach to Event Discovery from Single Images using MIL Framework." Web.
1. F. G. De Natale, G. Baoto, A. Rosani. A Hierarchical Approach to Event Discovery from Single Images using MIL Framework [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1362

TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION


This paper presents a novel method to track the hierarchical structure of Web video groups on the basis of salient keyword matching including semantic broadness estimation. To the best of our knowledge, this paper is the first work to perform extraction and tracking of the hierarchical structure simultaneously. Specifically, the proposed method first extracts the hierarchical structure of Web video groups and salient keywords of them on the basis of an improved scheme of our previously reported method.

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Authors:
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama
Submitted On:
6 December 2016 - 6:46pm
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harakawa_globalsip2016_poster.pdf

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[1] Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama, "TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1331. Accessed: Oct. 20, 2017.
@article{1331-16,
url = {http://sigport.org/1331},
author = {Ryosuke Harakawa;Takahiro Ogawa;Miki Haseyama },
publisher = {IEEE SigPort},
title = {TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION},
year = {2016} }
TY - EJOUR
T1 - TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION
AU - Ryosuke Harakawa;Takahiro Ogawa;Miki Haseyama
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1331
ER -
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. (2016). TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION. IEEE SigPort. http://sigport.org/1331
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama, 2016. TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION. Available at: http://sigport.org/1331.
Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. (2016). "TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION." Web.
1. Ryosuke Harakawa,Takahiro Ogawa,Miki Haseyama. TRACKING HIERARCHICAL STRUCTURE OF WEB VIDEO GROUPS BASED ON SALIENT KEYWORD MATCHING INCLUDING SEMANTIC BROADNESS ESTIMATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1331

A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION

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Authors:
HongLiu, Xiaohu Sun
Submitted On:
25 March 2016 - 12:35am
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pls_ranker_age_estimation_icassp2016_poster.pdf

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[1] HongLiu, Xiaohu Sun, "A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1040. Accessed: Oct. 20, 2017.
@article{1040-16,
url = {http://sigport.org/1040},
author = {HongLiu; Xiaohu Sun },
publisher = {IEEE SigPort},
title = {A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION},
year = {2016} }
TY - EJOUR
T1 - A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION
AU - HongLiu; Xiaohu Sun
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1040
ER -
HongLiu, Xiaohu Sun. (2016). A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION. IEEE SigPort. http://sigport.org/1040
HongLiu, Xiaohu Sun, 2016. A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION. Available at: http://sigport.org/1040.
HongLiu, Xiaohu Sun. (2016). "A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION." Web.
1. HongLiu, Xiaohu Sun. A PARTIAL LEAST SQUARES BASED RANKER FOR FAST AND ACCURATE AGE ESTIMATION [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1040

LDADeep+: Latent Aspect Discovery with Deep Representations


LDADeep+ utilizes the high-level meaning of deep learning representation, and combines it with topic model to learn good aspects

Nowadays, with the success and fast growth of social media communities and mobile devices, people are encouraged to share their multimedia data online. Analyzing and summarizing data into useful information thus becomes increasingly important. For on- line photo sharing services like Flickr, when users are uploading a batch of daily photos at a time, the tags users provided tend to be rather vague, containing only a small amount of information.

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Authors:
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu
Submitted On:
24 March 2016 - 12:09pm
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tsai_LDADeep_4_3 copy.pptx

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[1] Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu, "LDADeep+: Latent Aspect Discovery with Deep Representations", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1028. Accessed: Oct. 20, 2017.
@article{1028-16,
url = {http://sigport.org/1028},
author = {Chieh-En Tsai; Hui-Lan Hsieh; Winston Hsu },
publisher = {IEEE SigPort},
title = {LDADeep+: Latent Aspect Discovery with Deep Representations},
year = {2016} }
TY - EJOUR
T1 - LDADeep+: Latent Aspect Discovery with Deep Representations
AU - Chieh-En Tsai; Hui-Lan Hsieh; Winston Hsu
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1028
ER -
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. (2016). LDADeep+: Latent Aspect Discovery with Deep Representations. IEEE SigPort. http://sigport.org/1028
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu, 2016. LDADeep+: Latent Aspect Discovery with Deep Representations. Available at: http://sigport.org/1028.
Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. (2016). "LDADeep+: Latent Aspect Discovery with Deep Representations." Web.
1. Chieh-En Tsai, Hui-Lan Hsieh, Winston Hsu. LDADeep+: Latent Aspect Discovery with Deep Representations [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1028

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Oct. 20, 2017.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks


Multimedia event detection (MED) is the task of detecting given events (e.g. birthday party, making a sandwich) in a large collection of video clips. While visual features and automatic speech recognition typically provide the best features for this task, non-speech audio can also contribute useful information, such as crowds cheering, engine noises, or animal sounds.

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Authors:
Yun Wang, Leonardo Neves, Florian Metze
Submitted On:
17 March 2016 - 4:13pm
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2016.03 For ICASSP.ppt

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[1] Yun Wang, Leonardo Neves, Florian Metze, "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/753. Accessed: Oct. 20, 2017.
@article{753-16,
url = {http://sigport.org/753},
author = {Yun Wang; Leonardo Neves; Florian Metze },
publisher = {IEEE SigPort},
title = {Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks},
year = {2016} }
TY - EJOUR
T1 - Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks
AU - Yun Wang; Leonardo Neves; Florian Metze
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/753
ER -
Yun Wang, Leonardo Neves, Florian Metze. (2016). Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. IEEE SigPort. http://sigport.org/753
Yun Wang, Leonardo Neves, Florian Metze, 2016. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks. Available at: http://sigport.org/753.
Yun Wang, Leonardo Neves, Florian Metze. (2016). "Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks." Web.
1. Yun Wang, Leonardo Neves, Florian Metze. Audio-Based Multimedia Event Detection Using Deep Recurrent Neural Networks [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/753

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