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http://dx.doi.org/10.6109/jkiice.2020.24.3.384

Indoor Scene Classification based on Color and Depth Images for Automated Reverberation Sound Editing  

Jeong, Min-Heuk (School of Electronics and Information Engineering, Korea Aerospace University)
Yu, Yong-Hyun (School of Electronics and Information Engineering, Korea Aerospace University)
Park, Sung-Jun (School of Electronics and Information Engineering, Korea Aerospace University)
Hwang, Seung-Jun (School of Electronics and Information Engineering, Korea Aerospace University)
Baek, Joong-Hwan (School of Electronics and Information Engineering, Korea Aerospace University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.3, 2020 , pp. 384-390 More about this Journal
Abstract
The reverberation effect on the sound when producing movies or VR contents is a very important factor in the realism and liveliness. The reverberation time depending the space is recommended in a standard called RT60(Reverberation Time 60 dB). In this paper, we propose a scene recognition technique for automatic reverberation editing. To this end, we devised a classification model that independently trains color images and predicted depth images in the same model. Indoor scene classification is limited only by training color information because of the similarity of internal structure. Deep learning based depth information extraction technology is used to use spatial depth information. Based on RT60, 10 scene classes were constructed and model training and evaluation were conducted. Finally, the proposed SCR + DNet (Scene Classification for Reverb + Depth Net) classifier achieves higher performance than conventional CNN classifiers with 92.4% accuracy.
Keywords
Scene Classification; Depth Estimation; Reverberation; Convolutional Neural Network;
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