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Discovering Intersections of Music Genres With Machine Learning
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Author(s): Fiona Veseli (Rochester Institute of Technology, Kosovo), Orinda Visoka (Rochester Institute of Technology, Kosovo)and Erudit Jupolli (Rochester Institute of Technology, Kosovo)
Copyright: 2023
Pages: 17
Source title:
The Software Principles of Design for Data Modeling
Source Author(s)/Editor(s): Debabrata Samanta (Rochester Institute of Technology, Kosovo)
DOI: 10.4018/978-1-6684-9809-5.ch008
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Abstract
The music industry generates an enormous amount of data, which makes classifying and organizing that data into a genre a very difficult task. A potential solution to that problem is to cluster the music using machine learning. Machine learning algorithms might enhance personalized suggestions, search engines, and music categorization systems by creating a model which can precisely identify different genres relying on their acoustic and subjective properties. Recent research suggests that even though there is a large overlap across genres, with machine learning algorithms, we can properly categorize music genres by recognizing differences as well as similarities between them. In more general terms, grouping musical styles using machine learning has several uses in the music industry. It can speed up the identification of new musical styles and encourage cross-genre collaborations among musicians.
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