Open AccessMusic genre classification and music recommendation by using deep learning
Author(s) -
Elbir A.,
Aydin N.
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2019.4202
Subject(s) - computer science , pop music automation , active listening , set (abstract data type) , popular music , programming , music industry , music information retrieval , speech recognition , artificial neural network , recommender system , music education , artificial intelligence , music history , information retrieval , musical , visual arts , art , psychology , communication , programming language
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.