Measuring Audio Prompt Adherence with Distribution-based Embedding Distances

Understand

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.