Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/148567
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dc.titleRECOMMENDING MUSIC FOR LANGUAGE LEARNING: THE PROBLEM OF SINGING VOICE INTELLIGIBILITY
dc.contributor.authorKARIM MAGDI ABDELFATTAH IBRAHIM
dc.date.accessioned2018-10-31T18:00:51Z
dc.date.available2018-10-31T18:00:51Z
dc.date.issued2018-08-10
dc.identifier.citationKARIM MAGDI ABDELFATTAH IBRAHIM (2018-08-10). RECOMMENDING MUSIC FOR LANGUAGE LEARNING: THE PROBLEM OF SINGING VOICE INTELLIGIBILITY. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/148567
dc.description.abstractLearning a new language is a complex task that requires time dedication and continuous learning. Language immersion is a recommended approach to maximize the learning by using the foreign language in daily activities. Research has shown that listening to music in a foreign language can enhance the listener's language and enrich his/her vocabulary. In this study, we investigate how to recommend songs that maximize the language learner benefit from listening to songs in the target languages. Specifically, we are proposing a method for annotating songs according to their intelligibility to human listeners. We then propose a number of acoustic features that measure the different factors affecting intelligibility in singing voice and use them in building a system to automatically estimate the intelligibility of a given song. Finally, we study the usability of crowdsourcing platforms for collecting and annotating songs according to their intelligibility score on a large scale.
dc.language.isoen
dc.subjectMusic Information Retrieval, Singing Voice, Intelligibility, Music Recommendation, Language Learning
dc.typeThesis
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.supervisorWANG YE
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE
dc.identifier.orcid0000-0003-3315-9539
Appears in Collections:Master's Theses (Open)

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