Music popularity: Metrics, characteristics, and audio-based prediction

Junghyuk Lee, Jong Seok Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Understanding music popularity is important not only for the artists who create and perform music but also for the music-related industry. It has not been studied well how music popularity can be defined, what its characteristics are, and whether it can be predicted, which are addressed in this paper. We first define eight popularity metrics to cover multiple aspects of popularity. Then, the analysis of each popularity metric is conducted with long-term real-world chart data to deeply understand the characteristics of music popularity in the real world. We also build classification models for predicting popularity metrics using acoustic data. In particular, we focus on evaluating features describing music complexity together with other conventional acoustic features including MPEG-7 and Mel-frequency cepstral coefficient (MFCC) features. The results show that, although room still exists for improvement, it is feasible to predict the popularity metrics of a song significantly better than random chance based on its audio signal, particularly using both the complexity and MFCC features.

Original languageEnglish
Article number8327835
Pages (from-to)3173-3182
Number of pages10
JournalIEEE Transactions on Multimedia
Volume20
Issue number11
DOIs
Publication statusPublished - 2018 Nov

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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