@inproceedings{f6f841ecd668472ea91f6445e622e6e5,
title = "Environmental Sound Recognition Based on Residual Network and Stacking Algorithm",
abstract = "Environmental sound recognition is one of the important tasks in the field of audio research. Because the environment is complex and there is a lot of useless sound information, the traditional methods have low recognition accuracy, which is gradually replaced by related methods of deep learning. In this paper, combined with the latest research in this field, the recognition algorithm based on residual network and stacking method is proposed. The whole is divided into two parts: a feature extractor and a classifier. The residual network is responsible for extracting features with high recognition rate and the stacking algorithm is responsible for accurate recognition. The method is applied to the representative datasets ESC-50 and UrbanSound8k. We obtain a higher accuracy and the model is more clear and simple.",
keywords = "Environment sound recognition, MFCC, Residual network, Stacking algorithm",
author = "Haoyuan Wang and Xuemei Ren and Zhen Zhao",
note = "Publisher Copyright: {\textcopyright} 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; Chinese Intelligent Systems Conference, CISC 2020 ; Conference date: 24-10-2020 Through 25-10-2020",
year = "2021",
doi = "10.1007/978-981-15-8458-9_73",
language = "English",
isbn = "9789811584572",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "682--690",
editor = "Yingmin Jia and Weicun Zhang and Yongling Fu",
booktitle = "Proceedings of 2020 Chinese Intelligent Systems Conference - Volume II",
address = "Germany",
}