An Improved Metric of Informational Masking for Perceptual Audio Quality Measurement
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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.
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