Transfer learning for music classification and regression tasks using artist tags

Lei Wang, Hongning Zhu, Xulong Zhang, Shengchen Li, Wei Li*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

7 Citations (Scopus)

Abstract

In this paper, a transfer learning method that exploits artist tags for general-purpose music feature vector extraction is presented. The feature vector extracted from the last convolutional layer in a deep convolutional neural network (DCNN) trained with artist tags is showed for music classification and regression tasks. Not only are artist tags adequate in the music community, therefore easy to be gathered, but also contain much high-level abstract information about the artists and the music audio released by the artists. To train the network, a dataset containing 33903 30-second clips, annotated with artist tags was created. The model is trained to predict the artist tags from audio content first in the proposed system. Then the model is transferred to extract the features that are used to perform music genre classification and music emotion recognition tasks. The experiment results show that the features learned using artist tags under the context of transfer learning are able to be effectively applied in music genre classification and music emotion recognition tasks.

Original languageEnglish
Title of host publicationProceedings of the 7th Conference on Sound and Music Technology CSMT 2019, Revised Selected Papers
EditorsHaifeng Li, Lin Ma, Shengchen Li, Chunying Fang, Yidan Zhu
PublisherSpringer
Pages81-89
Number of pages9
ISBN (Print)9789811527555
DOIs
Publication statusPublished - 2020
Externally publishedYes
Event7th Conference on Sound and Music Technology, CSMT 2019 - Harbin, China
Duration: 26 Dec 201929 Dec 2019

Publication series

NameLecture Notes in Electrical Engineering
Volume635
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference7th Conference on Sound and Music Technology, CSMT 2019
Country/TerritoryChina
CityHarbin
Period26/12/1929/12/19

Keywords

  • Music emotion recognition
  • Music genre classification
  • Transfer learning

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