International Journal of Internet, Broadcasting and Communication

  • 제11권3호
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  • Pages.106-110
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  • 2019
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  • 2288-4920(pISSN)
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  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Deep Learning-Based Sound Localization Using Stereo Signals Based on Synchronized ILD

  • Hwang, Hyeon Tae (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology) ;
  • Yun, Deokgyu (Dept. of Electronic Engineering, Seoul National University of Science and Technology) ;
  • Choi, Seung Ho (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology)
  • 투고 : 2019.07.24
  • 심사 : 2019.08.05
  • 발행 : 2019.08.31
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초록

The interaural level difference (ILD) used for the sound localization using stereo signals is to find the difference in energy that the sound source reaches both ears. The conventional ILD does not consider the time difference of the stereo signals, which is a factor of lowering the accuracy. In this paper, we propose a synchronized ILD that obtains the ILD after synchronizing these time differences. This method uses the cross-correlation function (CCF) to calculate the time difference to reach both ears and use it to obtain synchronized ILD. In order to prove the performance of the proposed method, we conducted two sound localization experiments. In each experiment, the synchronized ILD and CCF or only the synchronized ILD were given as inputs of the deep neural networks (DNN), respectively. In this paper, we evaluate the performance of sound localization with mean error and accuracy of sound localization. Experimental results show that the proposed method has better performance than the conventional methods.

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참고문헌

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