International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Research Paper | Computer Science & Engineering | India | Volume 10 Issue 5, May 2021

Music Recommendation System

Nipun Prakash Gupta | Durgesh Kumar [2]

Abstract: Music recommendations seem to be just an invention among the industry to say about the rapid, ever-growing technological advances. However, while a reliable standard for similar genres is reliable, a track that suggests a supported format for music formats is uncommon owing to the large amount of song-related information on song streaming platforms, a song-compression system that supports the current user list on his gadget had better be established in such a way that conceived beside these lines on type endeavoring to search out new song track simpler all through this venture, we usually provide a way to display song tracks on the user's device and displays its properties such as genre, culture, emotions, language, rhythm, and tempo within the AI (AI) field, Machine Learning (ML) can be a visual, powerful, which mimics a process, not surprisingly used to find the problems of use and efficiency. The algorithmic guideline starts with a gathering of arrangements (preferred by the user's song) that usually type dynamic solutions that contain genres of music similar to those that the user has struggled with too many times or currently enjoying. Through the framework of music promotion, the music provider will expect and will in time provide relevant music to their customers who are attracted by the qualities of song tracks that has previously been acquired. Our experiment would like to expand the song recommendation gadget in an effort to provide suggestions for similar similarities in other ways to the audio signal. This looks at the usage of the know-how-to-output feature gadget to see similarity among other methods. Emerging tips are displayed on the user's display to listen.

Keywords: Client, content based, collaborative, item, music, recommendation, user

Edition: Volume 10 Issue 5, May 2021,

Pages: 1118 - 1123

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