Abstract
The accurate description of a complex process should take into account not only the interacting elements involved but also the scale of the description. Therefore, there can not be a single measure for describing the associated complexity of a process nor a single metric applicable in all scenarios. This article introduces a framework based on multiscale entropy to characterize the complexity associated with the most identifiable characteristic of songs: the melody. We are particularly interested in measuring the complexity of popular songs and identifying levels of complexity that statistically explain the listeners’ preferences. We analyze the relationship between complexity and popularity using a database of popular songs and their relative position in a preferences ranking. There is a tendency toward a positive association between complexity and acceptance (success) of a song that is, however, not significant after adjusting for multiple testing.
Highlights
We evaluate the multiscale entropy as a candidate to characterize the complexity associated with the melody of a musical segment
Since there were no words, in this case, the authors analyzed the information content of music by using the language associated with the Music Instrument Digital Interface (MIDI) format
The 100 top songs are identified for each year, and its multiscale entropy (MSE) is computed
Summary
Shanon’s proposed information metrics: entropy [7] This assessment of language’s information used words as the symbols making up languages. Since there were no words, in this case, the authors analyzed the information content of music by using the language associated with the Music Instrument Digital Interface (MIDI) format. This language contains all the necessary instructions to generate and reproduce the specified song. It was possible to estimate the informational entropy, among other useful metrics, and characterize the associated information content of the songs This characterization makes it possible to identify the musical genre and analyze changes in music’s complexity over time
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