Understanding differences between unique individuals is an important and emerging topic in auditory science and immersive experiences. Socio-cultural and anthropometric idiosyncrasies of listeners can lead to unintended auditory experiences, far from what media content creators intended. To better understand how this individuality may influence a listener’s preferences, we investigated various individually related factors, including previous listening experiences and cognitive profiles. In addition, we proposed a data-driven clustering method and showed its efficacy for meaningful grouping of listeners. In this study, we validated the data-driven method with 13 new subjects who generated attribute rating data for 16 stimulus conditions. The method, employing neural network clustering, successfully grouped participants into two preference-based categories with a 92.3% accuracy. The results support the proposed model’s reliability and its potential in applications to enhance individually optimized 3D music presentations.
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