An Acoustic Source Localization Method Using a Drone-Mounted Phased Microphone Array

Abstract

Currently, the detection of targets using drone-mounted imaging equipment is a very useful technique and is being utilized in many areas. In this study, we focus on acoustic signal detection with a drone detecting targets where sounds occur, unlike image-based detection. We implement a system in which a drone detects acoustic sources above the ground by applying a phase difference microphone array technique. Localization methods of acoustic sources are based on beamforming methods. The background and self-induced noise that is generated when a drone flies reduces the signal-to-noise ratio for detecting acoustic signals of interest, making it difficult to analyze signal characteristics. Furthermore, the strongly correlated noise, generated when a propeller rotates, acts as a factor that degrades the noise source direction of arrival estimation performance of the beamforming method. Spectral reduction methods have been effective in reducing noise by adjusting to specific frequencies in acoustically very harsh situations where drones are always exposed to their own noise. Since the direction of arrival of acoustic sources estimated from the beamforming method is based on the drone’s body frame coordinate system, we implement a method to estimate acoustic sources above the ground by fusing flight information output from the drone’s flight navigation system. The proposed method for estimating acoustic sources above the ground is experimentally validated by a drone equipped with a 32-channel time-synchronized MEMS microphone array. Additionally, the verification of the sound source location detection method was limited to the explosion sound generated from the fireworks. We confirm that the acoustic source location can be detected with an error performance of approximately 10 degrees of azimuth and elevation at the ground distance of about 150 m between the drone and the explosion location.

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

“…Then, the inflection point p i3D of the histogram H 3D is determined as the reference value, as in Equation (7).…”

“…In drone audition research, sound source localization (SSL) and sound source separation/enhancement (SSS/SSE) are often addressed. For example, Go et al attached four microphone arrays to the underside of a drone and localized the source of impulse sounds using beamforming [7]. Wang et al mounted a microphone array to the top of a drone and proposed a deep neural network (DNN)-based speech enhancement method [8].…”

“…The first is the development of microphone arrays. To increase the noise tolerance for SSL, microphone arrays consisting of different types and arrangements of microphones have been proposed [7,[10][11][12]. The second is the development of SSL methods.…”

Acoustic Signature and Impact of High-Speed Railway Vehicles in the Vicinity of Transport Routes

“…The beamforming method consists in processing a spatial-temporal signal recorded by a microphone array. Each microphone of the acoustic array, having a specific position relative to its centre, is assigned a time delay which allows it to focus the signal beam in the direction of acoustic wave propagation [5,19,[23][24][25][26][27]. By using a time delay, the measured signal is the same for all microphones used.…”

Deep-Learning-Assisted Sound Source Localization From a Flying Drone

“…Uni-modal approaches, such as spatial likelihood methods and spatial filtering methods, use the microphone signal only. Spatial likelihood methods are based on traditional source localization algorithms, such as steered response power (SRP) and steered response power with phase transform (SRP-PHAT) [3], [15]- [18], and multiple signal classification (MUSIC) [9], [19], [20]. While being Manuscript received: September 15, 2022 The authors are with Centre for Intelligent Sensing, Queen Mary University of London, London, UK (e-mail: {lin.wang, a.cavallaro}@qmul.ac.uk) widely used for ground robot audition, the performance of these algorithms typically degrades with drone platforms due to the strong ego-noise and hence the low SNR [21].…”