Music genre classification and music recommendation by using deep learning

Today, music is a very important and perhaps inseparable part of people's daily life. There are many genres of music and these genres are different from each other, resulting in people to have different preferences of music. As a result, it is an important and up-to-date issue to classify music...

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Veröffentlicht in:Electronics letters Jg. 56; H. 12; S. 627 - 629
Hauptverfasser: Elbir, A, Aydin, N
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 11.06.2020
Schlagworte:
acoustic features extraction
deep learning
deep neural network model
feature extraction
learning (artificial intelligence)
music
music acoustic characteristics
music genre classification system
music listening applications
music recommendation
neural nets
pattern classification
recommender systems
Signal processing
pattern classification
music acoustic characteristics
deep learning
recommender systems
music
music genre classification system
feature extraction
music recommendation
deep neural network model
music listening applications
acoustic features extraction
learning (artificial intelligence)
neural nets
ISSN:0013-5194, 1350-911X, 1350-911X
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Today, music is a very important and perhaps inseparable part of people's daily life. There are many genres of music and these genres are different from each other, resulting in people to have different preferences of music. As a result, it is an important and up-to-date issue to classify music and to recommend people new music in music listening applications and platforms. Classifying music by their genre is one of the most useful techniques used to solve this problem. There are a number of approaches for music classification and recommendation. One approach is based on the acoustic characteristics of music. In this study, a music genre classification system and music recommendation engine, which focuses on extracting representative features that have been obtained by a novel deep neural network model, have been proposed. Acoustic features extracted from these networks have been utilised for music genre classification and music recommendation on a data set.
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2019.4202