A principal component model to identify Turkish soundscapes’ affective attributes based on a corpus-driven approach

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buir.contributor.authorYılmazer, Semiha
buir.contributor.authorFasllija, Ela
buir.contributor.authorAlimadhi, Enkela
buir.contributor.authorŞahin, Zekiye
buir.contributor.authorMercan, Elif
buir.contributor.authorDalirnaghadeh, Donya
buir.contributor.orcidYılmazer, Semiha|0000-0001-9522-1102
buir.contributor.orcidFasllija, Ela|0000-0002-5631-029X
buir.contributor.orcidAlimadhi, Enkela|0000-0003-4998-0268
buir.contributor.orcidMercan, Elif|0000-0003-0037-8989
buir.contributor.orcidDalirnaghadeh, Donya|0000-0001-7971-9410
dc.citation.epage109410-12en_US
dc.citation.spage109410-1
dc.citation.volumeNumber209
dc.contributor.authorYılmazer, Semiha
dc.contributor.authorFasllija, Ela
dc.contributor.authorAlimadhi, Enkela
dc.contributor.authorŞahin, Zekiye
dc.contributor.authorMercan, Elif
dc.contributor.authorDalirnaghadeh, Donya
dc.date.accessioned2024-03-15T12:53:45Z
dc.date.available2024-03-15T12:53:45Z
dc.date.issued2023-06-30
dc.departmentDepartment of Interior Architecture and Environmental Design
dc.description.abstractThis study focused on achieving linguistic and culturally appropriate equivalents of Turkish soundscape attributes present in ISO 12913–3 by incorporating a Corpus-Driven Approach (CDA). A two-phase experiment was set up to find Turkish equivalents of affective quality attributes. The first phase consisted of the formation of a Corpus. An online questionnaire was prepared and sent to 196 native Turkish speakers from all around Türkiye to define adjectives. The second phase of the experiment was performed in a listening room. For this purpose, twenty-four binaural sound recordings were collected from seven public spaces. Afterward, forty individuals evaluated the recordings by using the attributes from Phase 1. The perceptual dimensions were obtained from the generated corpus in Turkish based on a rating scale by applying the Principal Component Analysis (PCA). Results indicated a two-dimensional model with two main components, Pleasantness and Eventfulness. Each component is associated with a main orthogonal axis denoted by ‘annoying-comfortable’ and ‘dynamic-uneventful,’ respectively. This circular organization of soundscape attributes is supported by two derived axes, namely ‘chaotic-calm’ and ‘monotonous-enjoyable’, rotated 45°on the same plane. Additionally, by using Spearman's rank correlation coefficient, sixty-four different bipolar adjective pairs were found. The adjective pairs showed that the highest correlations are mainly on the pleasant-unpleasant continuum, namely Component 1 of PCA. The collected data were also analyzed using Agglomerative Hierarchical Cluster analysis with the Ward method in R programming language to cluster the adjectives. The results inferred that there are four top-level categories. From the first to the fourth level, categories consisted of pleasant, uneventful, eventful, and annoying adjectives, respectively. Moreover, the terms grouped on the first cluster found their dichotomous on the fourth cluster, while maintaining the same relationship in the pleasant-unpleasant continuum.
dc.description.provenanceMade available in DSpace on 2024-03-15T12:53:45Z (GMT). No. of bitstreams: 1 A_principal_component_model_to_identify_turkish_soundscapes’_affective_attributes_based_on_a_corpus-driven_approach.pdf: 1839469 bytes, checksum: 2e23095abf30035817727d88270ca64c (MD5) Previous issue date: 2023-06-30en
dc.embargo.release2025-06-30
dc.identifier.doi10.1016/j.apacoust.2023.109410
dc.identifier.eissn1872-910X
dc.identifier.issn0003-682X
dc.identifier.urihttps://hdl.handle.net/11693/114808
dc.language.isoen_US
dc.publisherElsevier
dc.relation.isversionofhttps://doi.org/10.1016/j.apacoust.2023.109410
dc.rightsCC BY-NC-ND 4.0 Deed (Attribution-NonCommercial)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleApplied Acoustics
dc.subjectAgglomerative hierarchical clustering
dc.subjectCircumplex
dc.subjectCorpus-driven approach
dc.subjectPrincipal component analysis
dc.subjectSoundscape
dc.subjectTurkish quality attributes
dc.titleA principal component model to identify Turkish soundscapes’ affective attributes based on a corpus-driven approach
dc.typeArticle

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