Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Medhat, Fady"'
Autor:
Medhat, Fady
Sound recognition has been studied for decades to grant machines the human hearing ability. The advances in this field help in a range of applications, from industrial ones such as fault detection in machines and noise monitoring to household applica
Externí odkaz:
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.759903
Autor:
Medhat, Fady, Mohammadi, Mahnaz, Jaf, Sardar, Willcocks, Chris G., Breckon, Toby P., Matthews, Peter, McGough, Andrew Stephen, Theodoropoulos, Georgios, Obara, Boguslaw
Publikováno v:
IEEE International Conference on Big Data (Big Data) 2018
Handling large corpuses of documents is of significant importance in many fields, no more so than in the areas of crime investigation and defence, where an organisation may be presented with a large volume of scanned documents which need to be proces
Externí odkaz:
http://arxiv.org/abs/1904.12387
Publikováno v:
Artificial Intelligence XXXIV. SGAI 2017
The ConditionaL Neural Network (CLNN) exploits the nature of the temporal sequencing of the sound signal represented in a spectrogram, and its variant the Masked ConditionaL Neural Network (MCLNN) induces the network to learn in frequency bands by em
Externí odkaz:
http://arxiv.org/abs/1805.10004
Neural network based architectures used for sound recognition are usually adapted from other application domains, which may not harness sound related properties. The ConditionaL Neural Network (CLNN) is designed to consider the relational properties
Externí odkaz:
http://arxiv.org/abs/1804.02665
Publikováno v:
International Conference on Artificial Neural Networks (ICANN) Year: 2017
We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes into consideration the temporal nature of the sound signal and the MCLNN extends upon the CLNN
Externí odkaz:
http://arxiv.org/abs/1803.02421
Publikováno v:
International Conference on Neural Information Processing (ICONIP) Year: 2017, Pages: 470-481
The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The CLNN captures the conditional temporal influence between the frames in a window and the mask in th
Externí odkaz:
http://arxiv.org/abs/1802.06432
Publikováno v:
IEEE International Conference on Data Science and Advanced Analytics (DSAA) Year: 2017, Pages: 389 - 394
Deep neural network architectures designed for application domains other than sound, especially image recognition, may not optimally harness the time-frequency representation when adapted to the sound recognition problem. In this work, we explore the
Externí odkaz:
http://arxiv.org/abs/1802.05792
Publikováno v:
IEEE International Conference on Machine Learning and Applications (ICMLA) Year: 2017 Pages: 199 - 206
Automatic feature extraction using neural networks has accomplished remarkable success for images, but for sound recognition, these models are usually modified to fit the nature of the multi-dimensional temporal representation of the audio signal in
Externí odkaz:
http://arxiv.org/abs/1802.02617
Publikováno v:
IEEE International Conference on Data Mining (ICDM) Year: 2017 Pages: 979 - 984
Neural network based architectures used for sound recognition are usually adapted from other application domains such as image recognition, which may not harness the time-frequency representation of a signal. The ConditionaL Neural Networks (CLNN) an
Externí odkaz:
http://arxiv.org/abs/1801.05504
Publikováno v:
In Applied Soft Computing Journal May 2020 90