  Perceptual evaluation constitutes a crucial aspect of various
audio-processing tasks. Full reference (FR) or similarity-based metrics rely on
high-quality reference recordings, to which lower-quality or corrupted versions
of the recording may be compared for evaluation. In contrast, no-reference (NR)
metrics evaluate a recording without relying on a reference. Both the FR and NR
approaches exhibit advantages and drawbacks relative to each other. In this
paper, we present a novel framework called CORN that amalgamates these dual
approaches, concurrently training both FR and NR models together. After
training, the models can be applied independently. We evaluate CORN by
predicting several common objective metrics and across two different
architectures. The NR model trained using CORN has access to a reference
recording during training, and thus, as one would expect, it consistently
outperforms baseline NR models trained independently. Perhaps even more
remarkable is that the CORN FR model also outperforms its baseline counterpart,
even though it relies on the same training data and the same model
architecture. Thus, a single training regime produces two independently useful
models, each outperforming independently trained models
