4.7 Article

An Embedded Multichannel Sound Acquisition System for Drone Audition

期刊

IEEE SENSORS JOURNAL
卷 23, 期 12, 页码 13377-13386

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2023.3273330

关键词

Drone audition; ego-noise reduction; embedded system; microphone array

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This study presents an embedded multichannel sound acquisition and recording system with eight microphones mounted on a quadcopter. The system can record the sound from multiple microphones simultaneously, store the data locally for on-device processing, and transmit the multichannel audio via wireless communication for remote processing. Experimental results show that the ego-noise reduction performance of the microphone array varies depending on the array placement and the location of the target sound.
Microphone array techniques can improve the acoustic sensing performance of drones, compared with the use of a single microphone. However, multichannel sound acquisition systems are not available in current commercial drone platforms. We present an embedded multichannel sound acquisition and recording system with eight microphones mounted on a quadcopter. The system is developed based on Bela, an embedded computing system for audio processing. The system can record the sound from multiple microphones simultaneously, can store the data locally for on-device processing, and can transmit the multichannel audio via wireless communication to a ground terminal for remote processing. We disclose the technical details of the hardware and software design and development of the system. We implement two setups that place the microphone array at different locations on the drone body. We present experimental results obtained by the state-of-the-art drone audition algorithms applied to the sound recorded by the embedded system flying with a drone. It is shown that the ego-noise reduction performance achieved by the microphone array varies depending on the array placement and the location of the target sound. This observation provides valuable insights into hardware development for drone audition.

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