  Audio impairment recognition is based on finding noise in audio files and
categorising the impairment type. Recently, significant performance improvement
has been obtained thanks to the usage of advanced deep learning models.
However, feature robustness is still an unresolved issue and it is one of the
main reasons why we need powerful deep learning architectures. In the presence
of a variety of musical styles, hand-crafted features are less efficient in
capturing audio degradation characteristics and they are prone to failure when
recognising audio impairments and could mistakenly learn musical concepts
rather than impairment types. In this paper, we propose a new representation of
hand-crafted features that is based on the correlation of feature pairs. We
experimentally compare the proposed correlation-based feature representation
with a typical raw feature representation used in machine learning and we show
superior performance in terms of compact feature dimensionality and improved
computational speed in the test stage whilst achieving comparable accuracy.
