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Music genre detection using deep learning models

Authors: Kishore Mishra Ayush; Kumar Singh Divyansh; Khare Ankit;

Music genre detection using deep learning models

Abstract

An automated music classification system is that automates the entire process of music classification, i.e., classifying music without any human intervention. Along with this, it is very important to have a good recommendation system that will help with features such as music classification. This paper proposes an automated music classification system that will be very useful for both informational and entertainment purposes in the field of music. This system, based on a musicrelated Artificial Intelligence (AI) algorithm, automatically categorizes different types of music corresponding to different music genres, e.g., Hip Hop, Jazz, Rock, Blues, etc. Another feature added to this system is to recommend similar songs classified by the system. The architecture of the system and the algorithms used at each stage are described and implemented in this paper. The system also provides a lyrics classification module that generates and provides users with lyrics of the user choice. Compared to realistic musical classification, which has always been a difficult problem as it may lack structure or rationality.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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