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http://dx.doi.org/10.6109/jkiice.2019.23.12.1506

CNN based dual-channel sound enhancement in the MAV environment  

Kim, Young-Jin (Department of Computer Science & Engineering, Graduate School, Korea University of Technology and Education)
Kim, Eun-Gyung (School of Computer Science & Engineering, Korea University of Technology and Education)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.23, no.12, 2019 , pp. 1506-1513 More about this Journal
Abstract
Recently, as the industrial scope of multi-rotor unmanned aerial vehicles(UAV) is greatly expanded, the demands for data collection, processing, and analysis using UAV are also increasing. However, the acoustic data collected by using the UAV is greatly corrupted by the UAV's motor noise and wind noise, which makes it difficult to process and analyze the acoustic data. Therefore, we have studied a method to enhance the target sound from the acoustic signal received through microphones connected to UAV. In this paper, we have extended the densely connected dilated convolutional network, one of the existing single channel acoustic enhancement technique, to consider the inter-channel characteristics of the acoustic signal. As a result, the extended model performed better than the existed model in all evaluation measures such as SDR, PESQ, and STOI.
Keywords
Dual-Channel speech Enhancement; Unmanned Aerial Vehicle(UAV); Convolutional Neural Network(CNN); Dense Connectivity; Dilated Convolution;
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