Abstract
Although the difference between the fast Fourier transforms of two audio signals is often used as a basic measure of predicting perceived colouration, these signal measures do not provide information on how relevant the results are from a perceptual point of view. This paper presents a perceptually motivated loudness calculation for predicting the colouration between binaural signals which incorporates equal loudness frequency contouring, relative subjective loudness weighting, cochlea frequency modelling, and an iterative normalisation of input signals. The validation compares the presented model to three other colouration calculations in two ways: using test signals designed to evaluate specific elements of the model, and against the results of a listening test on degraded binaural audio signals. Results demonstrate the presented model is appropriate for predicting the colouration between binaural signals.
Highlights
The most definitive method of evaluating the perceptual difference between audio signals is through subjective listening tests using human participants
The Basic Spectral Difference (BSD), Composite Loudness Level (CLL), and Predicted Binaural Colouration (PBC) values are less correlated with the listening test results at lower levels of signal similarity, which could be an avenue for further work
This paper has presented a loudness method for predicting the perceived colouration between binaural signals using perceptually motivated signal processing techniques prior to the difference calculation
Summary
The most definitive method of evaluating the perceptual difference between audio signals is through subjective listening tests using human participants. Listening test paradigms typically involve the comparison of test signals and reference signals to determine either the existence of a perceivable difference or the magnitude of that perceived difference. Listening tests are demanding and costly to run as they require time to set up and conduct, as well as the participation and organisation of human subjects. It is desirable to consider objective quality metrics for audio signals as an alternative. These are especially useful in algorithm development and product prototyping phases, which often require fast and repeatable analysis
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