.png){kind=icon}
返回
CNN hyper-parameter optimization for environmental sound classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.apacoust.2022.109168.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Environmental sounds are being used widely in our lives. It is especially used in tasks such as managing smart cities, location determination, surveillance systems, machine hearing, and environmental monitoring. The main method for this, environmental sound classification (ESC), has been increasingly studied in recent years. However, the classification of these sounds is more difficult than other sounds because there are too many parameters that generate noise. The study tried to find the convolutional neural network (CNN) model that gave the highest accuracy for ESC tasks with the optimization of hyper-parameters. For this purpose, the Particle Swarm Optimization (PSO) algorithm was rearranged to represent the CNN architecture. Thus, the hyper-parameters in CNN are represented exactly without any transformation during optimization. Studies were carried out on the ESC-10, ESC-50, and Urbansound8k data sets, which are state-of-art for ESC tasks. Some data augmentation techniques have been used for data sets in the training of CNN models. The CNN models, which were obtained with PSO, achieved success rates of 98.64 % for ESC-10, 93.71 % for ESC-50, and 98.45 % for Urbansound8k, respectively. These results are the best accuracy values obtained with the pure CNN model when compared with previous studies. As a result, it has been made possible to automatically design CNN models for the classification of urban sounds, giving high classification accuracy. Thus, researchers who do not know much about CNN design can use this method in their desired datasets without the need for expert knowledge.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Environmental sound classification (ESC)
CNN
Particle swarm optimization (PSO)
Hyper -parameter optimization
Urbansound8k
ESC-50
期刊
IF:
3.6
论文数:
7.4K
被引数:
1.7W
机构
引用论文
Mental Health Screening in Addiction, Corrections and Social Service Settings: Validating the MMS
PREAI
分享收藏A novel spiral pattern and 2D M4 pooling based environmental sound classification method一种基于螺旋模式和二维M4池化的环境声分类方法.png)
APPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
分享收藏Attention based convolutional recurrent neural network for environmental sound classification基于注意力的卷积循环神经网络环境声音分类NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏A new pyramidal concatenated CNN approach for environmental sound classification一种新的金字塔级联CNN环境声音分类方法APPLIED ACOUSTICS
APPLIED ACOUSTICSIF3.6
PREAI
分享收藏
查看更多内容