A comparative study on prediction of the indoor soundscape in museums via machine learning

buir.contributor.authorYılmazer, Semiha
buir.contributor.authorYılmazer, Cengiz
buir.contributor.authorAcun, Volkan
dc.citation.epage11en_US
dc.citation.spage1en_US
dc.contributor.authorYılmazer, Semihaen_US
dc.contributor.authorYılmazer, Cengizen_US
dc.contributor.authorAcun, Volkanen_US
dc.coverage.spatialMadrid, Spainen_US
dc.date.accessioned2020-01-30T10:46:20Z
dc.date.available2020-01-30T10:46:20Z
dc.date.issued2019-06
dc.departmentDepartment of Interior Architecture and Environmental Designen_US
dc.descriptionDate of Conference: 16 -19 June 2019en_US
dc.descriptionConference name: INTER-NOISE 2019 MADRID - 48th International Congress and Exhibition on Noise Control Engineeringen_US
dc.description.abstractThis paper presents the preliminary findings of a soundscape research, which uses machine learning to make a prediction about human perception for indoor auditory environments. Museums of Çengelhan Rahmi Koc and Erim Tan are selected as the case study settings for data collection. The survey questionnaire basically consisted of three parts which are concerned with identifying the socio-cultural status, the personal tendencies, and evaluation of the physical and auditory environment. Before constructing of grounding the predictive model, data went through analyses to normalize and to eliminate the irrelevant items. Preliminary findings demonstrated how an indoor auditory environment would be perceived based on the individuals’ socio-cultural status, tendencies, preference and expectation from the space and physical elements of the space with together constructing a preliminary grounding model to use Machine / Deep learning algorithm.en_US
dc.identifier.urihttp://hdl.handle.net/11693/52928
dc.language.isoEnglishen_US
dc.publisherInstitute of Noise Control Engineering(INCE)en_US
dc.source.titleINTER-NOISE and NOISE-CON Congress and Conference Proceedings, InterNoise19en_US
dc.subjectIndoor soundscapeen_US
dc.subjectMachine learningen_US
dc.subjectArtificial intelligence (AI)en_US
dc.titleA comparative study on prediction of the indoor soundscape in museums via machine learningen_US
dc.typeConference Paperen_US
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