Abstract
ABSTRACT: Deep neural network that effectively capture the characteristics of data has been widely used in various applications. However, the amount of sound database is often insufficient for learning the deep neural network properly, so resulting in overfitting problems. In this paper, we propose a transfer learning framework that can effectively train the deep neural network even with insufficient sound event data by employing rich speech or music data. A series of experimental results verify that proposed method performs significantly better than the baseline deep neural network that was trained only with small sound event data.Keywords: Sound event classification, Transfer learning, Deep neural networkPACS numbers: 43.60.Bf, 43.72.Bs†Corresponding author: Hoirin Kim (hoirkim@kaist.ac.kr) School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea(Tel: 82-42-350-7617, Fax: 82-42-350-7619) 한국음향학회지 제 35권 제2호 pp. 143～148 (2016)The Journal of the Acoustical Society of Korea Vol.35, No.2 (2016)http://dx.doi.org/10.7776/ASK.2016.35.2.143
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