GDL

Journal from gdlhub / 2021-01-25 09:31:17
By : Suk-Hwan Jung, Yong-Joo Chung, Telkomnika
Created : 2021-01-25, with 1 files

Keyword : convolutional neural network; convolutional recurrent neural network; deep neural networks; feed forward neural network; recurent neural network; sound event detection;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14246
Document Source : web

We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined ranges. Among the implemented architectures, the CRNN performed best under all testing conditions, followed by CNN. Although RNN was effective in tracking the time-correlation information in audio signals,it exhibited inferior performance compared to the CNN and the CRNN. Accordingly, it is necessary to develop more optimization strategies for implementing RNN in sound event detection.

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