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.
Bibliographical noteFunding Information:
Manuscript received November 3, 2017; revised February 19, 2018; accepted March 15, 2018. Date of publication March 29, 2018; date of current version October 15, 2018. This research was supported by the Ministry of Science, ICT and Future Planning, South Korea, under the IT Consilience Creative Program (IITP-2017-2017-0-01015), supervised by the Institute for Information and Communications Technology Promotion. This paper was presented in part at the Third Workshop on Speech, Language and Audio in Multimedia, Brisbane, QLD, Australia, October 2015 . The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Yi-Hsuan Yang. (Corresponding author: Jong-Seok Lee.) The authors are with the School of Integrated Technology, Yonsei University, Seoul 03722, South Korea (e-mail:,firstname.lastname@example.org; jong-seok. email@example.com).
© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Signal Processing
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering