@inproceedings{9957161531704407890dd1102aae37b4,
title = "A survey of deep learning for polyphonic sound event detection",
abstract = "Deep learning has achieved state of the art in various machine learning problems, such as computer vision, speech recognition, and natural language processing. Sound event detection (SED), which is about recognizing audio events in real-life environments, has attracted a lot of attention recently. Many works have been successful when applying deep learning techniques for the SED problem as can be seen in Detection and Classification of Acoustic Scenes and Events (DCASE) challenge 2016-2017. In this paper, we present a review of the SED problem and discuss different deep learning approaches for the problem.",
keywords = "Convolutional neural networks, Deep learning, Neural networks, Recurrent neural networks, Sound event detection",
author = "An Dang and Vu, {Toan H.} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Orange Technologies, ICOT 2017 ; Conference date: 08-12-2017 Through 10-12-2017",
year = "2018",
month = apr,
day = "10",
doi = "10.1109/ICOT.2017.8336092",
language = "???core.languages.en_GB???",
series = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "75--78",
editor = "Lei Wang and Minghui Dong and Yanfeng Lu and Haizhou Li",
booktitle = "Proceedings of the 2017 International Conference on Orange Technologies, ICOT 2017",
}
