Placement Planning for Sound Source Tracking in Active Drone Audition

Abstract

This paper addresses a placement planning method for drones to improve the performance of source tracking by multiple drones equipped with microphone arrays. By equipping the drone with a microphone array, the drone will be able to locate the person in need of rescue, and by deploying multiple drones, the 3D location of the sound source can be estimated. However, effective drone placement for sound source tracking has not been well explored. Therefore, this paper proposes a new drone placement planning method to improve the performance of sound source tracking. By placing multiple drones close to the sound source with multiple angles, it is expected that tracking will be performed with small variance. The placement planning algorithm is also extended to be applicable to multiple sound sources. Through numerical simulations, it is confirmed that the proposed method reduces the sound source tracking error. In conclusion, the contribution of this research is to extend the field of drone audition to active drone audition that allows drones to move by themselves to achieve better tracking results.

A Performance Assessment on Rotor Noise-Informed Active Multidrone Sound Source Tracking Methods

“…The following sections describe each process. Flowchart describing the PAFIM method for each time step t. The array placement planning algorithm proposed in [30] (outlined in Section 3.3) is highlighted by the red boxes. Components of the rotor noise reduction method proposed in [32] (outlined in Section 3.4) are highlighted by the blue boxes.…”

“…Generally, when the sound source is surrounded (or bounded) by the drones, better tracking accuracy is achieved compared to when the drones are outside the vicinity of the drones [30], as the drones are less likely to be orthogonal with each other relative to the target sound source. Of course, the closeness of the drone's rotors to the microphone array also influences the input signal-to-noise ratio (SNR) given their high noise levels.…”

“…Considering these issues, the studies in [27,28] were further extended to consider the optimal placement of the drones/microphone arrays while performing SST [30]. Exploiting the fact that the drones are mobile and can be navigated, the study in [30] developed a drone placement algorithm to continuously optimise the placement of the microphone arrays by navigating the drones to maximise SST effectiveness (hereby referred to as array placement planning). Furthermore, the algorithm also enables the tracking of multiple sound sources by calculating the likelihood of the sound sources occurring based on the previous sound source location estimates of each sound source.…”

“…Furthermore, the algorithm also enables the tracking of multiple sound sources by calculating the likelihood of the sound sources occurring based on the previous sound source location estimates of each sound source. In other words, the study in [30] is one of the first studies to perform active drone audition in which the drones become active in the SST process. We note that while autonomous navigation for the optimal placement of multiple drones is not yet a widely explored area of study for sound source tracking, other applications have found significant performance improvement, such as 3D visual map reconstruction from aerial images [31].…”

“…With several techniques proposed in recent years, particularly those from [30,32], this study aims to integrate these proposed techniques and evaluate their performance improvements in SST based on [27,28]. This includes SST not only for single but also multiple sound sources.…”