@inproceedings{599bef22684e45ed9fe14751fcc5670b,
title = "Asymmetric kernel convolutional neural network for acoustic scenes classification",
abstract = "In this Zwork, Zwe propose an Asymmetric Kernel Convolutional Neural NetZwork (AKCNN) for Acoustic Scenes Classification (ASC). Its kernel shape is not the traditional square but asymmetric in Zwidth and height. It also uses Weight Normalization (WN) to accelerate the training process because it can early converge the training loss and accuracy. The best of all, WN can help increase the accuracy of ASC. TUT Acoustic Scenes 2016 Dataset [1] is used for evaluation. The result shoZws that AKCNN achieves accuracy 86.7%. If Zwe rank the score in DCASE2016 ASC Challenge, it shoZws that the system Zwould have a higher score than the 5th place.",
keywords = "Acoustic scenes classification, Convolutional neural network, Deep learning",
author = "Wang, {Chien Yao} and Wang, {Jia Ching} and Wu, {Yu Chi} and Chang, {Pao Chi}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 21st IEEE International Symposium on Consumer Electronics, ISCE 2017 ; Conference date: 14-11-2017 Through 15-11-2017",
year = "2017",
month = jul,
day = "2",
language = "???core.languages.en_GB???",
series = "Proceedings of the International Symposium on Consumer Electronics, ISCE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "11--12",
booktitle = "2017 IEEE International Symposium on Consumer Electronics, ISCE 2017",
}