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 Hybrid noise reduction for audio captured by drones
Tác giả hoặc Nhóm tác giả: Hyohoon Ahn, Duc Tai Le, Thien-Binh Dang, Siwon Kim, Hyunseung Choo
Nơi đăng: IMCOM '18: Proceedings of the 12th International Conference on Ubiquitous Information Management and Communication; Số: 10.1145/3164541.3175682;Từ->đến trang: 1-4;Năm: 2018
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
Recently, advances in technology have reduced the sizes, lowered prices, and increased commercial use of drones. In the market, drones have become an inexpensive and accessible way to accomplish many tasks such as filming, surveillance, agriculture, delivery, and broadcasting. Such applications typically require a video recording for their operations. However, due to the noise generated by propellers and rotors of a drone, the quality of sound in a video recorded by the drone is quite limited. In order to solve the noise problem of drones, this paper proposes a novel hybrid noise reduction technique combining Active Noise Control and Spectral subtraction methods. Experimental results show that the proposed technique produces a speech signal with up to 67.5% similarity to the original signal. It outperforms Active Noise Control and Spectral Subtraction with similarities of 53.1% and 39.6%, respectively.
ABSTRACT
Recently, advances in technology have reduced the sizes, lowered prices, and increased commercial use of drones. In the market, drones have become an inexpensive and accessible way to accomplish many tasks such as filming, surveillance, agriculture, delivery, and broadcasting. Such applications typically require a video recording for their operations. However, due to the noise generated by propellers and rotors of a drone, the quality of sound in a video recorded by the drone is quite limited. In order to solve the noise problem of drones, this paper proposes a novel hybrid noise reduction technique combining Active Noise Control and Spectral subtraction methods. Experimental results show that the proposed technique produces a speech signal with up to 67.5% similarity to the original signal. It outperforms Active Noise Control and Spectral Subtraction with similarities of 53.1% and 39.6%, respectively.
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