Sound Event Detection Based on Beamformed Convolutional Neural Network Using Multi-Microphones
- Authors
- Kim, Jaehun; Noh, Kyoungin; Kim, Jaeha; Chang, Joon Hyuk
- Issue Date
- Nov-2018
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Convolutional neural network; Deep neural network; Median filter; Parametric multi-channel Wiener filter
- Citation
- Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018, pp.170 - 173
- Indexed
- SCOPUS
- Journal Title
- Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018
- Start Page
- 170
- End Page
- 173
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/5247
- DOI
- 10.1109/ICNIDC.2018.8525597
- ISSN
- 2374-0272
- Abstract
- This paper presents a real environment sound event detection method based on pre-processing technology. Our goal is to improve the performance of the sound event detection using a pre-processing module called parameterized multi-channel non-causal Wiener filter (PMWF). First, we convert the existing 1 channel data to 2 channels through the Room impulse response generator (RIR) module. The reason for 2-channel conversion is that PMWF requires multiple channels for beamforming. Noise cancellation is performed through PMWF and the results are derived through the proposed convolutional neural network model. As a result, we found that this method has a good effect on real-time sound event detection, and we found that peak normalization and median filter also have a good effect.
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