  Perceptual audio quality measurement systems algorithmically analyze the
output of audio processing systems to estimate possible perceived quality
degradation using perceptual models of human audition. In this manner, they
save the time and resources associated with the design and execution of
listening tests (LTs). Models of disturbance audibility predicting peripheral
auditory masking in quality measurement systems have considerably increased
subjective quality prediction performance of signals processed by perceptual
audio codecs. Additionally, cognitive effects have also been known to regulate
perceived distortion severity by influencing their salience. However, the
performance gains due to cognitive effect models in quality measurement systems
were inconsistent so far, particularly for music signals. Firstly, this paper
presents an improved model of informational masking (IM) -- an important
cognitive effect in quality perception -- that considers disturbance
information complexity around the masking threshold. Secondly, we incorporate
the proposed IM metric into a quality measurement systems using a novel
interaction analysis procedure between cognitive effects and distortion
metrics. The procedure establishes interactions between cognitive effects and
distortion metrics using LT data. The proposed IM metric is shown to outperform
previously proposed IM metrics in a validation task against subjective quality
scores from large and diverse LT databases. Particularly, the proposed system
showed an increased quality prediction of music signals coded with bandwidth
extension techniques, where other models frequently fail.
