TY - JOUR
T1 - Drone Audition
T2 - Sound Source Localization Using On-Board Microphones
AU - Manamperi, Wageesha
AU - Abhayapala, Thushara D.
AU - Zhang, Jihui
AU - Samarasinghe, Prasanga N.
N1 - Publisher Copyright:
2329-9290 © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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 the multiple signal classification (MUSIC) based algorithms and the generalized cross-correlation with phase transformation (GCC-PHAT) method. 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.
AB - 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 the multiple signal classification (MUSIC) based algorithms and the generalized cross-correlation with phase transformation (GCC-PHAT) method. 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.
KW - Direction-of-arrival estimation
KW - Drones
KW - Estimation
KW - Location awareness
KW - Microphone arrays
KW - Multiple signal classification
KW - Propellers
UR - http://www.scopus.com/inward/record.url?scp=85122579466&partnerID=8YFLogxK
U2 - 10.1109/TASLP.2022.3140550
DO - 10.1109/TASLP.2022.3140550
M3 - Article
SN - 2329-9290
VL - 30
SP - 508
EP - 519
JO - IEEE/ACM Transactions on Audio Speech and Language Processing
JF - IEEE/ACM Transactions on Audio Speech and Language Processing
ER -