International Journal of Electrical, Electronics and Data Communication (IJEEDC)
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Volume-7,Issue-12  ( Dec, 2019 )
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Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 84
Paper Published : 1397
No. of Authors : 3784
  Journal Paper

Paper Title
A Frequency Response Normalization Method based on Deep Neural Networksfor IOT Devices

Abstract
This paper presents a novel frequency response normalization method for IoT(Internet of Things) devices, which is based on deep neural networks. When connecting the sounds acquired by different smart phones, unnatural soundsare generated, which is mainly due to different frequency responses for each smart phone. To solve this problem, we need to normalize the frequency responses of smart phones. In this study, we propose a normalization method using deep neural network. The input of the neural network is the spectrum of the smart phone sound to be processed and the output is the ratio of input to the spectrum of the reference smart phone sound. Experimental results show that when the acoustic signals acquired by different smart phones are connected, the naturalness of the sound is improved through objective and subjective evaluation. Keywords - Frequency normalization, IoT(Internet of Things), Virtual reality, User created contents, Deep neural network.


Author - Deokgyu Yun, Jaegyu Choi, Hanna Lee, Seung Ho Choi

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| Published on 2018-04-09
   
   
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