CNN Hyper-Parameter Optimization for Environmental Sound Classification
Yazarlar (1)
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | APPLIED ACOUSTICS |
| Dergi ISSN | 0003-682X Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q1 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2023 |
| Cilt No | 202 |
| Sayı | 109168 |
| Sayfalar | 109168 / 0 |
| DOI Numarası | 10.1016/j.apacoust.2022.109168 |
| Makale Linki | https://doi.org/10.1016/j.apacoust.2022.109168 |
| Özet |
| 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 … |
| Anahtar Kelimeler |
| Environmental sound classification (ESC) | CNN | Particle swarm optimization (PSO) | Hyper -parameter optimization | Urbansound8k | ESC-50 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 40 |
| Google Scholar | 77 |