@article {Kim:2023:0736-2935:6140,
title = "Neural-network clustering for evaluating immersive sound fields",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2023",
volume = "268",
number = "2",
publication date ="2023-11-30T00:00:00",
pages = "6140-6146",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2023/00000268/00000002/art00018",
doi = "doi:10.3397/IN_2023_0907",
author = "Kim, Sungyoung and Howie, Will",
abstract = "Evaluating immersive sound fields requires a group of trained and experienced listeners. This study investigates whether unsupervised clustering using a self-organizing map can be used to group listeners based on their perceptual responses to immersive reproduction of two orchestral
music recordings. The study trained a clustering model using 82 subjects' subjective responses, which consisted of five attribute ratings and one preference rating. Subsequently, the model was validated with a new data set from 19 subjects. The results showed that the model was 84% accurate
compared to manual clustering. The clustered group's cognitive characteristics were similar to a previous study, supporting the efficacy of the proposed neural-network clustering. The authors will continue to collect more data to further validate and update the model, which will reliably and
quickly evaluate a participant's degree of training and experience to determine their eligibility for relevant subjective evaluations of immersive auditory experiences.",
}