Transfer Asymmetry in Vehicle Pass-By Audio Synthesis: A Bidirectional Evaluation of PassByDiT and DopplerSim

Synthetic audio offers a potential remedy for scarce labeled vehicle pass-bys, but acoustic realism alone does not establish its usefulness as training data. We study this distinction through transfer asymmetry in vehicle speed estimation. We introduce PassByDiT, a physics-conditioned diffusion transformer, and DopplerSim, a source-spectrum recovery and rendering pipeline, and compare both with Pyroadacoustics under matched pass-by conditions. PassByDiT achieves the closest agreement with real recordings across most spectral and temporal measures, while the physics simulators align energy peaks more precisely with the requested closest-point-of-approach timing. A real-trained speed estimator achieves similar mean absolute errors on real and PassByDiT audio ($7.05$ and $7.80$ km/h, respectively), indicating that PassByDiT preserves motion cues usable by a model trained on real recordings. PassByDiT also provides the strongest synthetic-to-real speed-estimation performance among the evaluated synthesis methods. Bidirectional evaluation nevertheless reveals transfer asymmetry: agreement observed when testing a real-trained estimator on synthetic audio does not imply equivalent training utility in the reverse direction. Together, these findings establish complementary strengths of learned and physics-based synthesis and distinguish motion control, acoustic agreement, and training utility as separate dimensions of evaluation.