  The introduction of audio latent diffusion models possessing the ability to
generate realistic sound clips on demand from a text description has the
potential to revolutionize how we work with audio. In this work, we make an
initial attempt at understanding the inner workings of audio latent diffusion
models by investigating how their audio outputs compare with the training data,
similar to how a doctor auscultates a patient by listening to the sounds of
their organs. Using text-to-audio latent diffusion models trained on the
AudioCaps dataset, we systematically analyze memorization behavior as a
function of training set size. We also evaluate different retrieval metrics for
evidence of training data memorization, finding the similarity between mel
spectrograms to be more robust in detecting matches than learned embedding
vectors. In the process of analyzing memorization in audio latent diffusion
models, we also discover a large amount of duplicated audio clips within the
AudioCaps database.
