MUSIC STREAMING APP WITH DEEP LEARNING TECHNIQUES

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dc.contributor.authorMoldakhmetov, Nurbek
dc.contributor.authorTileubayev, Bibarys
dc.contributor.authorAlikhanov, Adilkhan
dc.contributor.authorDemeubay, Diar
dc.date.accessioned2024-06-19T04:58:06Z
dc.date.available2024-06-19T04:58:06Z
dc.date.issued2024-04-19
dc.description.abstractThis study presents the development of a music classification and recommendation system for web applications, employing deep learning and visualization methodologies to address the demand for refined audio recommendation systems. Through research and experimentation, high accuracy in music genre classification was achieved. Building a CNN model and employing t-SNE visualization resulted in a clear clustering of audio files. Coordinated of individual songs were used in the recommendation system logic.en_US
dc.identifier.citationMoldakhmetov, N., Alikhanov, A., Tileubayev, B., Demeubay, D. (2024). Music Streaming app with Deep Learning techniques. Nazarbayev University School of Engineering and Digital Sciencesen_US
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/7899
dc.language.isoenen_US
dc.publisherNazarbayev University School of Engineering and Digital Sciencesen_US
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectType of access: Restricteden_US
dc.titleMUSIC STREAMING APP WITH DEEP LEARNING TECHNIQUESen_US
dc.typeBachelor's thesisen_US
workflow.import.sourcescience

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