Reproducing the acoustic consequences of source or receiver motion from a set of discrete static room impulse responses (RIRs) is a fundamental cIRCAM:Galleryenge in spatial audio processing, with direct relevance to the generation of training and evaluation data for machine learning systems operating on reverberant speech and audio. This paper presents a comparative evaluation of three offline algorithms for acoustic motion simulation, two of which are ports of previously published methods and one of which is an independent implementation conceptually related to prior work. All three methods are evaluated against a common reference using two complementary validation approaches: an objective analysis based on interaural time difference (ITD) estimation from synthetic binaural signals, and a perceptual evaluation conducted under the MUSHRA protocol using stimuli drawn from a controlled moving-receiver database. Results indicate that frequency-domain interpolation between neighbouring impulse responses provides the most accurate binaural cue reproduction and the highest perceptual similarity to the reference under spectrally demanding stimuli, while nearest-neighbour switching produces the most pronounced artefacts under broadband excitation. Time-domain crossfading between fully convolved signals yields intermediate performance, achieving parity with frequency-domain interpolation for speech and noise but falling significantly behind for music. The combination of ITD-based objective analysis and MUSHRA perceptual evaluation proved informative in characterising method differences, and the two measures converged on a consistent performance ordering across methods.
