Active Noise Cancellation of Drone Propeller Noise through Waveform Approximation and Pitch-Shifting
Date of Award
5-8-2020
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Ashwin Ashok
Second Advisor
Anu Bourgeois
Third Advisor
Awad Mussa
Abstract
The use of drones introduces the problem of noise pollution due to the audio noise generated from its propeller rotations. To mitigate the noise pollution from drone propellers, this thesis explores a method of using active noise cancellation ANC. This thesis hypothesizes that by analyzing the waveform of the drone propeller noise, an approximated wave function can be produced and used as an anti-noise signal that can effectively nullify the drone noise. In order to align the phase of the anti-noise signal to maximize drone noise reduction, this thesis presents a signal pitch-shifting approach, to guide areas of destructive interference to a desired target such as a microphone, at a desired location. Through experimental evaluation using a prototype of the proposed Pitch-Aligned Active Noise Cancellation system PA-ANC, this thesis reveals that the proposed technique can achieve a 43.82% reduction of drone noise.
DOI
https://doi.org/10.57709/17621233
Recommended Citation
Narine, Michael, "Active Noise Cancellation of Drone Propeller Noise through Waveform Approximation and Pitch-Shifting." Thesis, Georgia State University, 2020.
doi: https://doi.org/10.57709/17621233
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