Journal article
A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection
Circuits, Systems, and Signal Processing, Vol.38(8), pp.3433-3353
2019
Abstract
Recently, neural network-based deep learning methods have been popularly applied to computer vision, speech signal processing and other pattern recognition areas. Remarkable success has been demonstrated by using the deep learning approaches. The purpose of this article is to provide a comprehensive survey for the neural network-based deep learning approaches on acoustic event detection. Different deep learning-based acoustic event detection approaches are investigated with an emphasis on both strongly labeled and weakly labeled acoustic event detection systems. This paper also discusses how deep learning methods benefit the acoustic event detection task and the potential issues that need to be addressed for prospective real-world scenarios.
Details
- Title
- A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection
- Authors/Creators
- X. Xia (Author/Creator) - The University of Western AustraliaR. Togneri (Author/Creator) - The University of Western AustraliaF. Sohel (Author/Creator) - Murdoch UniversityY. Zhao (Author/Creator) - The University of Western AustraliaD. Huang (Author/Creator) - The University of Western Australia
- Publication Details
- Circuits, Systems, and Signal Processing, Vol.38(8), pp.3433-3353
- Publisher
- Springer US
- Identifiers
- 991005544463707891
- Copyright
- © Springer Science+Business Media, LLC, part of Springer Nature 2019
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.174 Digital Signal Processing
- 4.174.152 Speech Recognition
- Web Of Science research areas
- Engineering, Electrical & Electronic
- ESI research areas
- Engineering