An increasing number of generative music models can be conditioned on an audio prompt that serves as musical context for which the model is to create an accompaniment (often further specified using a text prompt). Evaluation of how well model outputs adhere to the audio prompt is often done in a model or problem specific manner, presumably because no generic evaluation method for audio prompt adherence has emerged. Such a method could be useful both in the development and training of new models, and to make performance comparable across models. In this paper we investigate whether commonly used distribution-based distances like Fr\'echet Audio Distance (FAD), can be used to measure audio prompt adherence. We propose a simple procedure based on a small number of constituents (an embedding model, a projection, an embedding distance, and a data fusion method), that we systematically assess using a baseline validation. In a follow-up experiment we test the sensitivity of the proposed audio adherence measure to pitch and time shift perturbations. The results show that the proposed measure is sensitive to such perturbations, even when the reference and candidate distributions are from different music collections. Although more experimentation is needed to answer unaddressed questions like the robustness of the measure to acoustic artifacts that do not affect the audio prompt adherence, the current results suggest that distribution-based embedding distances provide a viable way of measuring audio prompt adherence. An python/pytorch implementation of the proposed measure is publicly available as a github repository.

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