Open AccessMusic genres classification by deep learning
Author(s) -
Yifeng Hu,
Gabriela Mogos
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i2.pp1186-1198
Subject(s) - python (programming language) , mel frequency cepstrum , computer science , feature extraction , digital audio , speech recognition , task (project management) , musical , deep learning , artificial neural network , artificial intelligence , multimedia , audio signal , engineering , art , visual arts , speech coding , systems engineering , operating system
Since musical genre is one of the most common ways used by people for managing digital music databases, music-genre-classification is a crucial task. There are many scenarios for its use, and the main one explored here is eventually being placed on Spotify, or Netease music, as an external component to recommend songs to users. This paper provides various deep neural networks developed based on python, together with the effect of these models on music genres classification. In addition, the paper illustrates the technologies for audio feature extraction in industrial environment by mel frequency cepstral coefficients (MFCC), audio data augmentation in