Classification of Environmental Sounds With Deep Learning

Yıl 2022, Cilt: 2 Sayı: 1, 20 - 28, 16.02.2022

Bekir Aksoy Uygar Usta Gürkan Karadağ Ali Rıza Kaya Melek Ömür

https://doi.org/10.54569/aair.1017801

Cited By: 2

Öz

Today, with the development of technology, environmental destruction is increasing day by day. For this reason, it is inevitable to take different measures to prevent the damage caused by environmental destruction. It is possible to prevent environmental damage by identifying the sounds that harm the environment and transferring them to the relevant units. In the study carried out, a data set of saw, rain, lightning, bark and broom sound data obtained from open access websites was created. Rain, barking and broom sounds in the data set were determined as the sounds that do not harm the environment, while saw and lightning were determined as the data set that harms the environment. The dataset was classified using VGG-13BN, ResNet-50 and DenseNet-121 deep learning architectures. When used, all three deep learning accuracy are due to over 95% study. Among these models, the VGG-13 BN model emerged as the most successful model with an accuracy rate of 99.72%.

Anahtar Kelimeler

Deep Learning, Transfer Learning, Spectrogram, Environmental Sound Detection

Kaynakça

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