Auscultation, particularly heart sound, is a non-invasive
technique that provides essential vital sign information.
Recently, self-supervised acoustic representation founda-
tion models (FMs) have been proposed to offer insights
into acoustics-based vital signs. However, there has been
little exploration of the extent to which auscultation is
encoded in these pre-trained FM representations. In this
work, using a publicly available phonocardioram (PCG)
dataset and a heart rate (HR) estimation model, we con-
duct a layer-wise investigation of six acoustic representa-
tion FMs: HuBERT, wav2vec2…
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