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CLASSIFICATION OF NOISE BETWEEN FLOORS IN A BUILDING USING PRE-TRAINED DEEP CONVOLUTIONAL NEURAL NETWORKS
Cited 9 time in
Web of Science
Cited 6 time in Scopus
- Authors
- Issue Date
- 2018-09
- Publisher
- IEEE
- Citation
- 2018 16TH INTERNATIONAL WORKSHOP ON ACOUSTIC SIGNAL ENHANCEMENT (IWAENC), pp.535-539
- Abstract
- 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.
- ISSN
- 2639-4316
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