Drone auralization model with statistical synthesis of amplitude and frequency modulations

Abstract

This paper presents a drone auralization model that reproduces the spectro-temporal and spatial characteristics of a drone during flight. Focusing on perceptual plausibility, the time-variant processes are modeled by taking into account the statistical amplitude and frequency modulation distributions of a reference drone sound. For completeness, the far-field directivity is extracted based on time-variant wave backpropagation from microphone array signals. Both components consider a combined level calibration with regard to the reconstructed sound pressure on a spherical surface around the source. With regard to reproducibility, this paper is accompanied by supplemental data to present a synthesis model including the oscillator and digital filter coefficients for procedural audio synthesis. From evaluation, the model shows good agreement by comparison of psychoacoustic measures of the synthesized drone to a recorded reference. The drone auralization model can be applied in future research on urban soundscapes where Unmanned Aerial Vehicles (UAV) may appear in a great variety of use cases. Furthermore, it can deliver input data for simulation tools where the spatial radiation characteristics of a drone should be included, such as the development of array-based drone detection.