This study tested a framework for modelling of sensory descriptors (words) differentiating headphones. Six descriptors were included in a listening test with recordings of the sound reproductions of seven prototype headphones. A comprehensive data quality analysis investigated both the performance of the listeners and the suitability of the descriptors for modelling. Additionally, two strategies were investigated for modelling metrics describing these descriptors, both relying on specific loudness estimations of the test stimuli. The stability of the initially found metrics was tested with a bootstrap procedure to quantify the potential of the metrics for future predictions within the perceptual space spanned by the headphones. The most promising results were metrics for Bass, Clean and Dark-Bright with correlations values of r^2 = 0.62, r^2 = 0.58, and r^2 = 0.90 respectively.
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https://aes2.org/publications/elibrary-page/?id=18360
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