Parameterisation, Localisation, and Enhancement of Spatial Soundfield from Drone On-Board Microphones

Abstract

This thesis explores the acoustic signal processing on drones which can enhance the drone audition functions for search and rescue missions, wildlife monitoring, and video recording for media and filming industries. With the recent emergence of drone-based applications and services, microphone arrays are beginning to be mounted to drones. However, audio capturing using drone-mounted microphone arrays leads to very low signal-to-drone noise ratio (SDNR) recordings (below negative 10 dB) due to the drone-generated motor and propeller noise signals. Hence, this causes a highly adverse noisy environment and degrades the quality and intelligibility of the recorded audio signals. Therefore, there is a growing interest in deriving new signal processing algorithms for the future deployment of the drone audition.

To this end, we develop novel methods for spatial soundfield modelling, and spatial audio processing using a microphone array embedded into the body of drones for sound source localisation and audio signal enhancement. To a lesser extent, drone noise characterisation is also studied.

We propose modelling of the drone noise harmonics as current to noise transfer function by taking the significant harmonics of the drone noise signature. The model is derived from the experimental measurements of a single motor propeller combination, and validated using extensive error analysis.

A cross-correlation-based DOA estimation method with drone noise angular spectrum subtraction is derived for acoustic source localisation. Our approach is to exploit the underlying spatial characteristics of drone noise to mitigate the noise emission by drones from the microphone recordings. The outcome resulting from this approach enables direction estimation of a single and simultaneously active multiple sound sources. Compared to the baseline approaches, the proposed localisation method is shown to be more robust to extreme SDNR conditions (up to negative 30 dB).

A framework of an audio signal enhancement using Wiener filtering approaches is developed with a substantial reduction of drone noise, and an improved signal quality and intelligibility of the target source signal. Application of this method is illustrated with extensive real data through experimental measurements for both (i) speech, and (ii) bird calls.

An efficient parameterisation of the spatial acoustic response to an on-board microphone of the drone due to a sound source, called the drone related transfer function, is introduced using spherical harmonics. This parameterisation is derived from the circular harmonic coefficients using a discrete set of measurements by multiple measurement points on circular arrays. The proposed approach models the scattering and diffraction response of a drone to an incident soundfield that can be used to enhance drone audition functions. Show more