Music genre classification and music recommendation by using deep learning

ELBİR A., Aydin N.

ELECTRONICS LETTERS, vol.56, no.12, pp.627-629, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 56 Issue: 12
  • Publication Date: 2020
  • Doi Number: 10.1049/el.2019.4202
  • Journal Name: ELECTRONICS LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.627-629
  • Keywords: learning (artificial intelligence), feature extraction, neural nets, music, recommender systems, pattern classification, music genre classification system, music recommendation, deep learning, music listening applications, music acoustic characteristics, deep neural network model, acoustic features extraction
  • Yıldız Technical University Affiliated: Yes

Abstract

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.