Rethinking environmental sound classification using convolutional neural networks: optimized parameter tuning of single feature extraction
The classification of environmental sounds is important for emerging applications such as automatic audio surveillance, audio forensics, and robot navigation. Existing techniques combined multiple features and stacked many CNN layers (very deep learning) to reach the desired accuracy. Instead of usi...
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| 主要な著者: | , , |
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| フォーマット: | 論文 |
| 言語: | English English |
| 出版事項: |
Springer Nature
2021
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| 主題: | |
| オンライン･アクセス: | http://irep.iium.edu.my/90215/7/90215_Rethinking%20environmental%20sound%20classification%20using%20convolutional%20neural%20networks_SCOPUS.pdf http://irep.iium.edu.my/90215/8/90215_Rethinking%20environmental%20sound%20classification%20using%20convolutional%20neural%20networks.pdf http://irep.iium.edu.my/90215/ https://link.springer.com/article/10.1007/s00521-021-06091-7 https://doi.org/10.1007/s00521-021-06091-7 |
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http://irep.iium.edu.my/90215/7/90215_Rethinking%20environmental%20sound%20classification%20using%20convolutional%20neural%20networks_SCOPUS.pdfhttp://irep.iium.edu.my/90215/8/90215_Rethinking%20environmental%20sound%20classification%20using%20convolutional%20neural%20networks.pdf
http://irep.iium.edu.my/90215/
https://link.springer.com/article/10.1007/s00521-021-06091-7
https://doi.org/10.1007/s00521-021-06091-7