Although the common procedure for measuring sound quality is based on subjective listening tests, this approach is labor intensive, requiring a large number of trained subjects and specialized facilities. If a model-based approach could approximate subjective tests, quality measurement could be automated. This study examines the degree to which objective measures from auditory models might improve the prediction of quality ratings by normal-hearing subjects with different types of distortion. Using a wide variety of audio data, the authors compared two sound quality assessment models based on PEMO-Q and CASP and one extended version of PEMO-Q to state-of-the-art quality-assessment models. The results revealed that the suggested quality models were able to predict a high range of different distortions and performed best compared to other state-of-the-art quality measures. In general, quality measures employing valid auditory models generalized best across different kinds of distortions.
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