Drone Audition: Sound Source Localization Using On-Board Microphones
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Manamperi, Wageesha
Abhayapala, Thushara
Zhang, Jihui (Aimee)
Samarasinghe, Prasanga
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IEEE Signal Processing Society
Abstract
This paper presents a sound source localization method using an irregular microphone array embedded in a drone. Sound source localization is an integral function of drone audition systems which enables various applications of drones such as search and rescue missions. However, the audio recordings using the on-board microphones obscure the sound emitted by a source on the ground due to drone generated motor and propeller noise, thus leading to an extremely low signal-to-drone noise ratio (SdNR). In this paper, we propose a cross-correlation based direction of arrival (DOA) estimation technique using the time difference of arrival (TDOA) at different microphone pairs, with noise angular spectrum subtraction. Through the measured current-specific drone noise spectrum, noise suppression has been achieved from the multi-channel recordings. Experimental results show that the proposed method is capable of estimating the position in three-dimensional space for simultaneously active multiple sound sources on the ground at low SdNR conditions (-30 dB), and localize two sound sources located at a certain azimuth angular separation with low prediction error comparable to traditional multiple signal classification (MUSIC) algorithm. Due to its simplicity, applicability to any array geometry, and better robustness against drone noise, the proposed method increases the feasibility of localization under extreme SdNR levels.
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IEEE/ACM Transactions on Audio, Speech, and Language Processing
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2099-12-31
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