Perceptual Evaluation of Audio Quality

It utilizes software to simulate perceptual properties of the human ear and then integrates multiple model output variables into a single metric.

They take into account knowledge of human auditory perception and typically achieve a reduced bit rate by ignoring audio information that is not likely to be heard by most listeners.

A psychoacoustic model must be used to predict how the information is masked by louder audio content adjacent in time and frequency.

Because the human auditory system is highly non-linear, noise levels vary with time and frequency characteristics of the audio signal.

The next image represents a simple block diagram of the relationship between the human audio system and an objective psychoacoustic model.

In the final stage the model output variables are combined using a neural network (weights defined in standard) to produce a result that copes with subjective quality assessment.

The Advanced version is computationally more demanding and may deliver slightly more accurate results; it uses FFT and filter banks to produce more MOVs for the neural network to work with.

BS.1387 is protected by several patents and is available under license together with the original code for commercial applications according to ITU fair, reasonable, and non-discriminatory terms.