Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141755
Title: An efficient dilated convolutional neural network for UAV noise reduction at low input SNR
Authors: Tan, Zhi-Wei
Nguyen, Anh Hai Trieu
Khong, Andy Wai Hoong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2020
Source: Tan, Z.-W., Nguyen, A. H. T., & Khong, A. W. H. (2019). An efficient dilated convolutional neural network for UAV noise reduction at low input SNR. Proceedings of 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 1885-1892. doi:10.1109/APSIPAASC47483.2019.9023324
Project: MRP14
Abstract: Acoustic applications on a multi-rotor unmanned aerial vehicle (UAV) have been hindered by its low input signal-to-noise ratio (SNR). Such low SNR condition poses prominent challenges for beamforming algorithms, statistical methods, and existing mask-based deep learning algorithms. We propose the small model on low SNR (SMoLnet), a compact convolutional neural network (CNN) to suppress UAV noise in noisy speech signals recorded off a microphone array mounted on the UAV. The proposed SMoLnet employs a large analysis window to achieve high spectral resolution since the loud UAV noise exhibits a narrow-band harmonic pattern. In the proposed SMoLnet model, exponentially-increasing dilated convolution layers were adopted to capture the global relationship across the frequency dimension. Furthermore, we performed direct spectral mapping between noisy and clean complex spectrogram to cater to the low SNR scenario. Simulation results show that the proposed SMoLnet outperforms existing dilation-based models in terms of speech quality and objective speech intelligibility metrics for UAV noise reduction. In addition, the proposed SMoLnet requires fewer parameters and achieves lower latency than the compared models.
URI: https://hdl.handle.net/10356/141755
ISBN: 978-1-7281-3249-5
ISSN: 2640-009X
DOI: 10.1109/APSIPAASC47483.2019.9023324
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/APSIPAASC47483.2019.9023324
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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