CnnSound: Convolutional Neural Networks for the Classification of Environmental Sounds
Yazarlar (2)
Doç. Dr. Özkan İNİK
Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | The 4th International Conference on Advances in Artificial Intelligence (ICAAI 2020) |
| Kongre Tarihi | 13-11-2020 / 15-11-2020 |
| Basıldığı Ülke | İngiltere |
| Basıldığı Şehir | London |
| Bildiri Linki | http://dl.acm.org/citation.cfm?id=3441417 |
| Özet |
| The classification of environmental sounds (ESC) has been increasingly studied in recent years. The main reason is that environmental sounds are part of our daily life, and associating them with our environment that we live in is important in several aspects as ESC is used in areas such as managing smart cities, determining location from environmental sounds, surveillance systems, machine hearing, environment monitoring. The ESC is however more difficult than other sounds because there are too many parameters that generate background noise in the ESC, which makes the sound more difficult to model and classify. The main aim of this study is therefore to develop more robust convolution neural networks architecture (CNN). For this purpose, 150 different CNN-based models were designed by changing the number of layers and values of their tuning parameters used in the layers. In order to test the accuracy … |
| Anahtar Kelimeler |
