  Over the past few decades, computational methods have been developed to
estimate perceptual audio quality. These methods, also referred to as objective
quality measures, are usually developed and intended for a specific application
domain. Because of their convenience, they are often used outside their
original intended domain, even if it is unclear whether they provide reliable
quality estimates in this case. This work studies the correlation of well-known
state-of-the-art objective measures with human perceptual scores in two
different domains: audio coding and source separation. The following objective
measures are considered: fwSNRseg, dLLR, PESQ, PEAQ, POLQA, PEMO-Q,
ViSQOLAudio, (SI-)BSSEval, PEASS, LKR-PI, 2f-model, and HAAQI. Additionally, a
novel measure (SI-SA2f) is presented, based on the 2f-model and a BSSEval-based
signal decomposition. We use perceptual scores from 7 listening tests about
audio coding and 7 listening tests about source separation as ground-truth data
for the correlation analysis. The results show that one method (2f-model)
performs significantly better than the others on both domains and indicate that
the dataset for training the method and a robust underlying auditory model are
crucial factors towards a universal, domain-independent objective measure.
