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How potential users of music search and retrieval systems describe the semantic quality of music.

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Title: How potential users of music search and retrieval systems describe the semantic quality of music.
Authors: Lesaffre, Micheline1, Voogdt, Liesbeth De1, Leman, Marc1, Baets, Bernard De2, Meyer, Hans De3, Martens, Jean-Pierre4
Source: Journal of the American Society for Information Science & Technology. Mar2008, Vol. 59 Issue 5, p695-707. 13p. 4 Charts, 2 Graphs.
Subjects: Music websites, Digital library use studies, Musical analysis, Semantics, Musical perception, Information storage & retrieval systems
Abstract: A large-scale study was set up aiming at the clarification of the influence of demographic and musical background on the semantic description of music. Our model for rating high-level music qualities distinguishes between affective/emotive, structural and kinaesthetic descriptors. The focus was on the understanding of the most important attributes of music in view of the development of efficient search and retrieval systems. We emphasized who the users of such systems are and how they describe their favorite music. Particular interest went to inter-subjective similarities among listeners. The results from our study suggest that gender, age, musical expertise, active musicianship, broadness of taste and familiarity with the music have an influence on the semantic description of music. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Lesaffre%2C+Micheline%22">Lesaffre, Micheline</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Voogdt%2C+Liesbeth+De%22">Voogdt, Liesbeth De</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Leman%2C+Marc%22">Leman, Marc</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Baets%2C+Bernard+De%22">Baets, Bernard De</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Meyer%2C+Hans+De%22">Meyer, Hans De</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Martens%2C+Jean-Pierre%22">Martens, Jean-Pierre</searchLink><relatesTo>4</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Society+for+Information+Science+%26+Technology%22">Journal of the American Society for Information Science & Technology</searchLink>. Mar2008, Vol. 59 Issue 5, p695-707. 13p. 4 Charts, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Music+websites%22">Music websites</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+library+use+studies%22">Digital library use studies</searchLink><br /><searchLink fieldCode="DE" term="%22Musical+analysis%22">Musical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Musical+perception%22">Musical perception</searchLink><br /><searchLink fieldCode="DE" term="%22Information+storage+%26+retrieval+systems%22">Information storage & retrieval systems</searchLink>
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  Data: A large-scale study was set up aiming at the clarification of the influence of demographic and musical background on the semantic description of music. Our model for rating high-level music qualities distinguishes between affective/emotive, structural and kinaesthetic descriptors. The focus was on the understanding of the most important attributes of music in view of the development of efficient search and retrieval systems. We emphasized who the users of such systems are and how they describe their favorite music. Particular interest went to inter-subjective similarities among listeners. The results from our study suggest that gender, age, musical expertise, active musicianship, broadness of taste and familiarity with the music have an influence on the semantic description of music. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of the American Society for Information Science & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=30081011
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        Value: 10.1002/asi.20731
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        Text: English
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      – SubjectFull: Musical analysis
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              Text: Mar2008
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