Asymmetric Kernel Convolutional Neural Network for Acoustic Scenes Classification

被引：0

作者：

Wang, Chien-Yao ^{[1
]}

Wang, Jia-Ching ^{[1
]}

Wu, Yu-Chi ^{[2
]}

Chang, Pao-Chi ^{[2
]}

机构：

[1] Natl Cent Univ, Dept Comp Sci & Informat Engn, Jhongli, Taiwan

[2] Natl Cent Univ, Dept Commun Engn, Jhongli, Taiwan

来源：

2017 IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS (ISCE) | 2017年

关键词：

acoustic scenes classification; deep learning; convolutional neural network;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this work, we propose an Asymmetric Kernel Convolutional Neural Network (AKCNN) for Acoustic Scenes Classification (ASC). Its kernel shape is not the traditional square but asymmetric in width 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 shows that AKCNN achieves accuracy 86.7%. If we rank the score in DCASE2016 ASC Challenge, it shows that the system would have a higher score than the 5th place.

引用

页码：11 / 12

页数：2

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