CLASSIFICATION OF NOISE BETWEEN FLOORS IN A BUILDING USING PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORKS

被引：0
|
作者：
Choi, Hwiyong [1 ]
Lee, Seungjun [1 ]
Yang, Haesang [1 ]
Seong, Woojae [1 ]
机构：
[1] Seoul Natl Univ, Dept Naval Architecture & Ocean Engn, Seoul, South Korea
来源：
2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC) | 2018年
关键词：
Noise between floors; auditory scene classification; pre-trained convolutional neural networks; mel-spectrogram;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper suggests a method for source location and type classification of noise between floors at an apartment complex, which is a serious social conflict issue in Korea. Pre-trained convolutional neural networks proposed by visual geometry group is adapted and used for the task. A dataset for evaluation of method is generated and gathered in a building. The dataset is converted to log scaled mel-spectrograms to be fed into the input of the networks. The method is evaluated via k-fold cross validation. For comparison of performance depending on network architecture, convolutional neural networks suggested by Salamon and Bello [IEEE Signal Process. Lett. 24, 279-283 (2017)] is employed and validated. Also, the effectiveness of pre-training is measured.
引用
收藏
页码：535 / 539
页数：5
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