  Audio applications involving environmental sound analysis increasingly use
general-purpose audio representations, also known as embeddings, for transfer
learning. Recently, Holistic Evaluation of Audio Representations (HEAR)
evaluated twenty-nine embedding models on nineteen diverse tasks. However, the
evaluation's effectiveness depends on the variation already captured within a
given dataset. Therefore, for a given data domain, it is unclear how the
representations would be affected by the variations caused by myriad
microphones' range and acoustic conditions -- commonly known as channel
effects. We aim to extend HEAR to evaluate invariance to channel effects in
this work. To accomplish this, we imitate channel effects by injecting
perturbations to the audio signal and measure the shift in the new (perturbed)
embeddings with three distance measures, making the evaluation domain-dependent
but not task-dependent. Combined with the downstream performance, it helps us
make a more informed prediction of how robust the embeddings are to the channel
effects. We evaluate two embeddings -- YAMNet, and OpenL3 on monophonic
(UrbanSound8K) and polyphonic (SONYC-UST) urban datasets. We show that one
distance measure does not suffice in such task-independent evaluation. Although
Fr\'echet Audio Distance (FAD) correlates with the trend of the performance
drop in the downstream task most accurately, we show that we need to study FAD
in conjunction with the other distances to get a clear understanding of the
overall effect of the perturbation. In terms of the embedding performance, we
find OpenL3 to be more robust than YAMNet, which aligns with the HEAR
evaluation.
