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Automated MP3 Tag Editor via Data Mining: A Classification Software for Predicting MP3 Metadata
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Author(s): Jonathan Rufus Samuel (Vellore Institute of Technology, India), Shivansh Sahai (Vellore Institute of Technology, India), P. Swarnalatha (Vellore Institute of Technology, India), Prabu Sevugan (Pondicherry University, India)and V. Balaji (Vardhaman College of Engineering, India)
Copyright: 2023
Pages: 18
Source title:
Handbook of Research on Deep Learning Techniques for Cloud-Based Industrial IoT
Source Author(s)/Editor(s): P. Swarnalatha (Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, India)and S. Prabu (Department Banking Technology, Pondicherry University, India)
DOI: 10.4018/978-1-6684-8098-4.ch012
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Abstract
The music space in today's world is ever evolving and expanding. With great improvements to today's technology, we have been able to bring out music to the vast majority of today's ever-growing and tech-savvy people. In today's market, the biggest players for music streaming include behemoth corporations like Spotify, Gaana, Apple Music, YouTube Music, and so on and so forth. This also happens to be quite the shift from how music was once listened to. For songs downloaded out of old music databases without the song's metadata in place, and other distribution sites, they oftentimes come without any known metadata, i.e., most of the details with regards to the songs are absent, such as the artist's name, the year it was made, album art, etc. This chapter discusses how data mining, data scraping, and data classification are utilized to help add incomplete metadata to song files without the same, along with the design process, the software development, and research for the same.
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