Leo McCormack,
Archontis Politis,
Ville Pulkki
Parametric spatial audio effects based on the multi-directional decomposition of Ambisonic sound scenes
Companion page for the 23rd International Conference on Digital Audio Effects, Vienna, Austria, 2021.
Abstract
Decomposing a sound-field into its individual components and respective parameters can represent a convenient first-step towards offering the user an intuitive means of controlling spatial audio effects and sound-field modification tools. The majority of such tools available today, however, are instead limited to linear combinations of signals or employ a basic single-source parametric model. Therefore, the purpose of this paper is to present a parametric framework, which seeks to overcome these limitations by first dividing the sound-field into its multi-source and ambient components based on estimated spatial parameters. It is then demonstrated that by manipulating the spatial parameters prior to reproducing the scene, a number of sound-field modification and spatial audio effects may be realised; including: directional warping, listener translation, sound source tracking, spatial editing workflows and spatial side-chaining. Many of the effects described have also been implemented as real-time audio plug-ins, in order to demonstrate how a user may interact with such tools in practice.
Overview
It is demonstrated that through manipulations of spatial parameters, as analysed by the Coding and Multi-Parameterisation of Ambisonic Sound Scenes (COMPASS) method [1], several spatial effects and sould-field editing workflows can be realised.
VST downloads
To demonstrate how the described spatial effects and sould-field editing workflows can be implemented and interacted with in practice, five VST plug-ins have been developed and released with the plug-in suite found here.
References
[1] Politis, A., Tervo S., and Pulkki, V. (2018) COMPASS: Coding and Multidirectional Parameterization of Ambisonic Sound Scenes. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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