In room acoustics, this relationship is typically simplified using the assumption of a linear, time-invariant (LTI) system. The room is assumed to behave consistently over time, and its effect on sound can be captured by measuring its transfer function, usually in the form of a room impulse response (IR). Convolving a dry signal with that IR allows us to reproduce how the signal would sound at a specific position, relative to a listener, within that room. This framework allows us to quantify and design spaces in ways that are both physically grounded and practically useful. For many acoustic engineering questions, the assumption of time invariance is entirely sufficient.
The real world, however, is rarely static.
We fidget. We pace while talking. We put our phone on speaker while washing the dishes. The moment either the listener or the source starts moving, the system is no longer strictly time-invariant. Propagation delays change continuously, early reflection patterns morph, and directional cues shift as a function of movement. This is where traditional room-acoustic methods using static impulse responses begin to reach their limit. Impulse responses describe unique places, not unique trajectories.
Last year, as a simulation specialist working on advanced room-acoustic modelling at Treble Technologies, I was tasked with building a prototype for a client that expanded our delivered results from static acoustic snapshots to more complex acoustic scenes.
That prototype demonstrated enough potential that my colleagues and I spent the following months integrating the approach natively into the Treble SDK. The core idea is to treat motion as a continuous trajectory through an acoustic field rather than as a sequence of independent snapshots. By carefully interpolating timing, energy, and spatial characteristics across precomputed positions, we preserve perceptual continuity while remaining faithful to the underlying physics of sound propagation. Crucially, this interpolation must operate at a resolution fine enough to match human (and machine) sensitivity to motion, particularly in ego-centric listening scenarios.
The resulting feature supports moving sources or receivers, directive sources, and a range of receiver formats, from mono to ambisonics and device rendering. Trajectories can be generated automatically, regenerated at different speeds after simulation, and combined with more complex scene definitions.
