DynamicSound simulator for simulating moving sources and microphone arrays

TL;DR

DynamicSound addresses the need for large, realistic multichannel audio data with moving sources for spatial-audio research. It introduces a physically grounded, time-domain model that jointly accounts for time-of-flight delays, Doppler shifts, geometric spreading, air absorption, and first-order reflections via an image-source approach, enabling continuous 3D motion and arbitrary microphone arrays. The framework is implemented in a modular, open-source architecture and validated against existing tools, demonstrating improved temporal causality and spatial fidelity in dynamic scenarios and seamless integration with DOA pipelines. This tool provides a practical, reproducible platform for training and evaluating beamforming, localization, and classification algorithms in open-field environments.

Abstract

Developing algorithms for sound classification, detection, and localization requires large amounts of flexible and realistic audio data, especially when leveraging modern machine learning and beamforming techniques. However, most existing acoustic simulators are tailored for indoor environments and are limited to static sound sources, making them unsuitable for scenarios involving moving sources, moving microphones, or long-distance propagation. This paper presents DynamicSound an open-source acoustic simulation framework for generating multichannel audio from one or more sound sources with the possibility to move them continuously in three-dimensional space and recorded by arbitrarily configured microphone arrays. The proposed model explicitly accounts for finite sound propagation delays, Doppler effects, distance-dependent attenuation, air absorption, and first-order reflections from planar surfaces, yielding temporally consistent spatial audio signals. Unlike conventional mono or stereo simulators, the proposed system synthesizes audio for an arbitrary number of virtual microphones, accurately reproducing inter-microphone time delays, level differences, and spectral coloration induced by the environment. Comparative evaluations with existing open-source tools demonstrate that the generated signals preserve high spatial fidelity across varying source positions and acoustic conditions. By enabling the generation of realistic multichannel audio under controlled and repeatable conditions, the proposed open framework provides a flexible and reproducible tool for the development, training, and evaluation of modern spatial audio and sound-source localization algorithms.

Figures (11)

• Figure 1: Air absorption at 20 °C, 1 atm, and 50% relative humidity, showing the total attenuation curve together with the individual oxygen and nitrogen relaxation contributions, computed according to ISO 9613-1 international1993acoustics.
• Figure 2: Illustration of the image-source method used to model reflections, showing the real source S, its mirrored image source S', and the resulting direct and reflected propagation paths to the receiver R.
• Figure 3: Class diagram of the DynamicSound simulator architecture, illustrating the overall software structure and the interactions between the main classes. The diagram highlights the separation into distinct packages: microphones, sources, environment, and acoustics.
• Figure 4: Drone trajectory generated using AirSimshah2017airsim, sampled at 1-second intervals throughout the physical simulation.
• Figure 5: Interpolated drone trajectory produced using the DynamicSound simulator, providing a smooth path reconstruction from the original 1-second AirSim samples.