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Perceptual audio quality measurement systems algorithmically analyze theoutput of audio processing systems to estimate possible perceived qualitydegradation using perceptual models of human audition. In this manner, theysave the time and resources associated with the design and execution oflistening tests (LTs). Models of disturbance audibility predicting peripheralauditory masking in quality measurement systems have considerably increasedsubjective quality prediction performance of signals processed by perceptualaudio codecs. Additionally, cognitive effects have also been known to regulateperceived distortion severity by influencing their salience. However, theperformance gains due to cognitive effect models in quality measurement systemswere inconsistent so far, particularly for music signals. Firstly, this paperpresents an improved model of informational masking (IM) -- an importantcognitive effect in quality perception -- that considers disturbanceinformation complexity around the masking threshold. Secondly, we incorporatethe proposed IM metric into a quality measurement systems using a novelinteraction analysis procedure between cognitive effects and distortionmetrics. The procedure establishes interactions between cognitive effects anddistortion metrics using LT data. The proposed IM metric is shown to outperformpreviously proposed IM metrics in a validation task against subjective qualityscores from large and diverse LT databases. Particularly, the proposed systemshowed an increased quality prediction of music signals coded with bandwidthextension techniques, where other models frequently fail.

An Improved Metric of Informational Masking for Perceptual Audio Quality Measurement