  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.
