scholarly journals Deep Learning For Audio

2020 ◽  
Author(s):  
Sai Priyamka Kotha ◽  
Sravani Nallagari ◽  
Jinan Fiaidhi
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Sound Classification ◽  
Learning Capabilities

Speech is the most efficient and convenient way of communication. The learning capabilities of the deep learning architecture can be used to develop the sound classification system to overcome the efficiency issues of the traditional systems. We propose to develop a model that classifies the audio of the speaker.

scholarly journals Deep Learning For Audio

2020 ◽  
Author(s):  
Sai Priyamka Kotha ◽  
Sravani Nallagari ◽  
Jinan Fiaidhi
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Sound Classification ◽  
Learning Capabilities

Speech is the most efficient and convenient way of communication. The learning capabilities of the deep learning architecture can be used to develop the sound classification system to overcome the efficiency issues of the traditional systems. We propose to develop a model that classifies the audio of the speaker.

Developing a Deep Learning Sound Classification System for a Smart Farming

2020 ◽  
Vol MA2020-01 (26) ◽  
pp. 1853-1853
Author(s):  
Oleksii Kudin ◽  
Anastasiia Kryvokhata ◽  
Vitaliy Ivanovich Gorbenko
Keyword(s):  
Deep Learning ◽  
Classification System ◽  
Sound Classification ◽  
Smart Farming

scholarly journals A primer on deep learning and convolutional neural networks for clinicians

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lara Lloret Iglesias ◽  
Pablo Sanz Bellón ◽  
Amaia Pérez del Barrio ◽  
Pablo Menéndez Fernández-Miranda ◽  
David Rodríguez González ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Capabilities ◽  
Computer Vision Applications ◽  
Fully Connected ◽  
Imaging Perspective ◽  
Adaptive Momentum

AbstractDeep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of convolutional neural networks has drastically improved the learning capabilities of computer vision applications, being able to directly consider raw data without any prior feature extraction. Advanced methods in the machine learning field, such as adaptive momentum algorithms or dropout regularization, have dramatically improved the convolutional neural networks predicting ability, outperforming that of conventional fully connected neural networks. This work summarizes, in an intended didactic way, the main aspects of these cutting-edge techniques from a medical imaging perspective.

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