A Survey: Neural Network-Based Deep Learning for Acoustic Event Detection

被引:21
作者
Xia, Xianjun [1 ]
Togneri, Roberto [1 ]
Sohel, Ferdous [2 ]
Zhao, Yuanjun [1 ]
Huang, Defeng [1 ]
机构
[1] Univ Western Australia, Sch Elect Elect & Comp Engn, 35 Stirling Hwy, Perth, WA 6009, Australia
[2] Murdoch Univ, Sch Engn & Informat Technol, 90 South St, Murdoch, WA 6150, Australia
关键词
Deep learning; Acoustic event detection; Strongly labeled; Weakly labeled; CLASSIFICATION;
D O I
10.1007/s00034-019-01094-1
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:3433 / 3453
页数:21
相关论文
共 93 条
[1]  
[Anonymous], 2015, P 2015 INT JOINT C N, DOI [DOI 10.1109/IJCNN.2015.7280624, 10.1109/IJCNN.2015.7280624]
[2]  
[Anonymous], 2017, P 31 INT C NEURAL IN, DOI DOI 10.5555/3295222.3295408
[3]  
[Anonymous], 2017, ARXIV170602291
[4]  
[Anonymous], 2013, ARXIV13125663
[5]  
[Anonymous], MULTIMODAL TECHNOLOG
[6]  
[Anonymous], 2016, P DET CLASS AC SCEN
[7]  
[Anonymous], IEEE WORKSH APPL SIG
[8]  
[Anonymous], P WORKSH DET CLASS A
[9]  
[Anonymous], 2015, Nature, DOI [10.1038/nature14539, DOI 10.1038/NATURE14539]
[10]  
[Anonymous], MATRIX INFORM GEOMET