Abstract
Drone audition techniques are helpful for listening to target sound sources from the sky, which can be used for human searching tasks in disaster sites. Among many techniques required for drone audition, sound source tracking is an essential technique, and thus several tracking methods have been proposed. Authors have also proposed a sound source tracking method that utilizes multiple microphone arrays to obtain the likelihood distribution of the sound source locations. These methods have been demonstrated in benchmark experiments. However, the performance against various sound sources with different distances and signal-to-noise ratios (SNRs) has been less evaluated. Since drone audition often needs to listen to distant sound sources and the input acoustic signal generally has a low SNR due to drone noise, making a performance assessment against source distance and SNR is essential. Therefore, this paper presents a concrete evaluation of sound source tracking methods using numerical simulation, focusing on various source distances and SNRs. The simulated results captured how the tracking performance will change when the sound source distance and SNR change. The proposed approach based on location distribution estimation tended to be more robust against distance increase, while existing approaches based on directional estimation tended to be more robust against decreasing SNR.
Highlights
Drones have recently been required to be used at disaster sites
We evaluated the performance of sound source tracking among several tracking methods that can be used in drone audition
We focused on the performance difference against source distance and signal-to-noise ratios (SNRs)
Summary
Drones have recently been required to be used at disaster sites. Drone audition techniques enable drones to find targets in low light conditions and have higher quality scene analysis with sound recognition techniques. Microphone arrays are commonly used since they can estimate sound source directions by calculating the time difference of arrival between microphones. One example of drone audition applications is to carry out people searching tasks [1,2,3]. By estimating the sound direction of people calling for help, drones will be able to find them even if they are covered in rubble. The main goal of this people searching task is to localize a stationary sound source (the person to be rescued), but only localizing stationary sound sources is not enough to accomplish this task. In addition to localization of stationary sources, tracking moving sound sources and sound source separation are required
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