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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Yong-Joo Chung
Keimyung University
Computer Science & IT Electrical & Electronics Engineering

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Sound event detection using deep neural networks Suk-Hwan Jung; Yong-Joo Chung
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.14246

Abstract

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.
Performance analysis of the convolutional recurrent neural network on acoustic event detection Suk-Hwan Jung; Yong-Joo Chung
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (641.466 KB) | DOI: 10.11591/eei.v9i4.2230

Abstract

In this study, we attempted to find the optimal hyper-parameters of the convolutional recurrent neural network (CRNN) by investigating its performance on acoustic event detection. Important hyper-parameters such as the input segment length, learning rate, and criterion for the convergence test, were determined experimentally. Additionally, the effects of batch normalization and dropout on the performance were measured experimentally to obtain their optimal combination. Further, we studied the effects of varying the batch data on every iteration during the training. From the experimental results using the TUT sound events synthetic 2016 database, we obtained optimal performance with a learning rate of 1/10000.  We found that a longer input segment length aided performance improvement, and batch normalization was far more effective than dropout. Finally, performance improvement was clearly observed by varying the starting points of the batch data for each iteration during the training.
Co-Authors Suk-Hwan Jung Suk-Hwan Jung