Deep Learning Algorithms Enabling Event Detection: A Review

DOI

10.2991/aisr.k.220201.030How to use a DOI?

Keywords

Artificial intelligence; Event detection; Machine learning; Deep learning; Neural networks

Abstract

Deep Learning has revolutionized computer vision, natural language processing, speech recognition, and information retrieval. However, as deep learning models developed, their parameter count, latency, and resource requirements rose. As a result, a model’s footprint, as well as quality, should always be addressed. Academics and industry have identified neural network-based deep learning as a possible research subject. Deep learning algorithms have had tremendous results. This paper will review neural networks’ deep learning methods for auditory event detection. For this reason, this paper aims to examine both highly and weakly labelled acoustic event detection systems based on deep learning. This article also discusses how deep learning might help detect events and the challenges in upcoming real-world scenarios. We briefly define the issue of model efficiency in deep learning, then cover the foundational work in the five core areas of model efficiency (modelling approaches, infrastructure, and hardware).

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Cherifa Nakkach
AU  - Amira Zrelli
AU  - Tahar Ezzeddine
PY  - 2022
DA  - 2022/02/02
TI  - Deep Learning Algorithms Enabling Event Detection: A Review
BT  - Proceedings of the 2nd International Conference on Industry 4.0 and Artificial Intelligence (ICIAI 2021)
PB  - Atlantis Press
SP  - 170
EP  - 175
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.220201.030
DO  - 10.2991/aisr.k.220201.030
ID  - Nakkach2022
ER  -