Algorithms for Assessing the Subjectivity of Perceptually Weighted Audible Errors
Auditory modeling is increasingly used to provide an objective prediction of the subjective performance of audio systems. Such techniques typically compare the predicted auditory stimulation of original and processed audio signals, to produce an estimate of audible error, which can be presented as an error surface. This paper investigates the use of audible error surfaces as a diagnostic tool, and provides an algorithmic interpretation to predict subjective opinion. The applicability of the technique across a range of industries is discussed.
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