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Helard Becerra Martinez
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2020 – today
- 2024
- [c14]Helard Becerra Martinez, Katryna Cisek, Alejandro García-Rudolph, John D. Kelleher, Andrew Hines:
Transparently Predicting Therapy Compliance of Young Adults Following Ischemic Stroke. xAI (4) 2024: 24-41 - [i3]Helard Becerra Martinez, Alessandro Ragano, Diptasree Debnath, Asad Ullah, Crisron Rudolf Lucas, Martin Walsh, Andrew Hines:
Dialogue Understandability: Why are we streaming movies with subtitles? CoRR abs/2403.15336 (2024) - 2022
- [c13]Helard Becerra Martinez, Alessandro Ragano, Andrew Hines:
Exploring the influence of fine-tuning data on wav2vec 2.0 model for blind speech quality prediction. INTERSPEECH 2022: 4088-4092 - [c12]Helard Becerra Martinez, Andrew Hines, Mylène C. Q. Farias:
See hear now: is audio-visual QoE now just a fusion of audio and video metrics? QoMEX 2022: 1-4 - 2021
- [c11]André H. M. Costa, Helard Becerra Martinez, Daniel G. Silva, Mylène C. Q. Farias:
Analyzing the effect of adding temporal features to an autoencoder-based video quality model. IQSP 2021 - [c10]Helard Becerra Martinez, André H. M. da Costa, Bruna Azambuja, Andrew Hines, Mylène C. Q. Farias:
Exploring the boundaries of an AE-based quality model: a performance analysis via synthetic content. IQSP 2021 - 2020
- [j3]Helard Becerra Martinez, Andrew Hines, Mylène C. Q. Farias:
UnB-AV: An Audio-Visual Database for Multimedia Quality Research. IEEE Access 8: 56641-56649 (2020) - [c9]Helard Becerra Martinez, Mylène C. Q. Farias, Andrew Hines:
Analyzing the performance of autoencoder-based objective quality metrics on audio-visual content. IQSP 2020 - [c8]Helard Becerra Martinez, Andrew Hines, Mylène C. Q. Farias:
How Deep is Your Encoder: An Analysis of Features Descriptors for an Autoencoder-Based Audio-Visual Quality Metric. QoMEX 2020: 1-6 - [i2]Helard Becerra Martinez, Mylène C. Q. Farias, Andrew Hines:
NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder. CoRR abs/2001.11406 (2020) - [i1]Helard Becerra Martinez, Andrew Hines, Mylène C. Q. Farias:
How deep is your encoder: an analysis of features descriptors for an autoencoder-based audio-visual quality metric. CoRR abs/2003.11100 (2020)
2010 – 2019
- 2019
- [c7]Helard Becerra Martinez, Mylène C. Q. Farias, Andrew Hines:
NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder. EUSIPCO 2019: 1-5 - [c6]Helard Becerra Martinez, Mylène C. Q. Farias, Andrew Hines:
A No-Reference Autoencoder Video Quality Metric. ICIP 2019: 1755-1759 - [c5]Helard Becerra Martinez, Mylène C. Q. Farias:
Analyzing the influence of cross-modal IP-based degradations on the perceived audio-visual quality. IQSP 2019 - 2018
- [j2]Helard Becerra Martinez, Mylène C. Q. Farias:
Combining audio and video metrics to assess audio-visual quality. Multim. Tools Appl. 77(18): 23993-24012 (2018) - [c4]Helard Becerra Martinez, Mylène C. Q. Farias, Andrew Hines:
Perceived quality of audio-visual stimuli containing streaming audio degradations. EUSIPCO 2018: 2529-2533 - [c3]Helard Becerra Martinez, Mylène C. Q. Farias:
Using The Immersive Methodology to Assess The Quality of Videos Transmitted in UDP and TCP-Based Scenarios. IQSP 2018 - 2014
- [j1]Helard Becerra Martinez, Mylène C. Q. Farias:
Full-reference audio-visual video quality metric. J. Electronic Imaging 23(6): 061108 (2014) - [c2]Helard Becerra Martinez, Mylène C. Q. Farias:
A no-reference audio-visual video quality metric. EUSIPCO 2014: 2125-2129 - [c1]Helard Becerra Martinez, Mylène C. Q. Farias:
An objective model for audio-visual quality. IQSP 2014: 90160P
Coauthor Index
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last updated on 2024-10-23 20:32 CEST by the dblp team
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