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pro vyhledávání: '"O'Connor, Mark"'
We present a novel approach to the 3D sound source localization task for distributed ad-hoc microphone arrays by formulating it as a set-to-set regression problem. By training a multi-modal masked autoencoder model that operates on audio recordings a
Externí odkaz:
http://arxiv.org/abs/2408.15771
Publikováno v:
Proc. Interspeech 2022, 1791-1795
Speaker localization using microphone arrays depends on accurate time delay estimation techniques. For decades, methods based on the generalized cross correlation with phase transform (GCC-PHAT) have been widely adopted for this purpose. Recently, th
Externí odkaz:
http://arxiv.org/abs/2208.04654
While the Transformer architecture has become ubiquitous in the machine learning field, its adaptation to 3D shape recognition is non-trivial. Due to its quadratic computational complexity, the self-attention operator quickly becomes inefficient as t
Externí odkaz:
http://arxiv.org/abs/2204.03957
Autor:
O'Connor, Mark J., Ding, Xinyi, Hernandez, Camila, Hubacz, Lisa, Church, Richard J., O'Connor, Laurel
Publikováno v:
In Endocrine Practice February 2024 30(2):122-127
Publikováno v:
Proc. Interspeech 2021, 4249-4253
The Transformer architecture has been successful across many domains, including natural language processing, computer vision and speech recognition. In keyword spotting, self-attention has primarily been used on top of convolutional or recurrent enco
Externí odkaz:
http://arxiv.org/abs/2104.00769
Autor:
Abdellatif, Alaa Awad, Samara, Lutfi, Mohamed, Amr, Erbad, Aiman, Chiasserini, Carla Fabiana, Guizani, Mohsen, O'Connor, Mark Dennis, Laughton, James
Epidemic situations typically demand intensive data collection and management from different locations/entities within a strict time constraint. Such demand can be fulfilled by leveraging the intensive and easy deployment of the Internet of Things (I
Externí odkaz:
http://arxiv.org/abs/2012.14294
Publikováno v:
2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 2740-2747
Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown
Externí odkaz:
http://arxiv.org/abs/2006.15864
Akademický článek
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Autor:
Zelceski, Anabel, Francica, Paola, Lingg, Lea, Mutlu, Merve, Stok, Colin, Liptay, Martin, Alexander, John, Baxter, Joseph S., Brough, Rachel, Gulati, Aditi, Haider, Syed, Raghunandan, Maya, Song, Feifei, Sridhar, Sandhya, Forment, Josep V., O’Connor, Mark J., Davies, Barry R., van Vugt, Marcel A.T.M., Krastev, Dragomir B., Pettitt, Stephen J., Tutt, Andrew N.J., Rottenberg, Sven, Lord, Christopher J.
Publikováno v:
In Cell Reports 30 May 2023 42(5)
Autor:
Chen, Rebecca, Ajwani, Shilpi, Christian, Bradley, Phelan, Claire, Srinivas, Ravi, Kenny, Josephine, O'Connor, Mark, Clarke, Kara, Sohn, Woosung, Yaacoub, Albert
Publikováno v:
Journal of Patient-Reported Outcomes; 8/19/2024, Vol. 8 Issue 1, p1-9, 9p