# Music Popularity: Metrics, Characteristics, and Audio-Based Prediction

## TL;DR

This paper defines multiple music popularity metrics, analyzes their real-world characteristics, and demonstrates that acoustic features, especially complexity and MFCC, can predict popularity better than chance.

## Contribution

It introduces eight new popularity metrics, analyzes their characteristics with chart data, and develops models that predict popularity from audio features, highlighting the importance of complexity and MFCC features.

## Key findings

- Popularity metrics can be predicted from audio signals.
- Complexity and MFCC features are particularly effective for prediction.
- Prediction models outperform random chance significantly.

## 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.