Rethinking environmental sound classification using convolutional neural networks: optimized parameter tuning of single feature extraction

• Main Authors: Al-Hattab, Yousef Abd, Mohd Zaki, Hasan Firdaus, Shafie, Amir Akramin
• Format: Article
• Language: English; English
• Published: Springer Nature 2021
• Subjects: TK7885 Computer engineering
• Online Access: 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

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...