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Towards a Measure for Characterizing the Informational Content of Audio Signals and the Relation between Complexity

Daniel Guerrero1, Pedro Rivera2, Gerardo Febres3

  • 1Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.

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Summary
This summary is machine-generated.

This study introduces multiscale entropy to measure song melody complexity. While complexity shows a tendency to correlate with song popularity, this association was not statistically significant after adjustments.

Keywords:
auditory encodingentropyinformation contentmultiscale complexitymusic

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Area of Science:

  • Music Information Retrieval
  • Complexity Science
  • Psychoacoustics

Background:

  • Describing complex processes requires considering multiple scales.
  • A single complexity metric is insufficient for all scenarios.
  • Melody is a key characteristic of songs, influencing listener perception.

Purpose of the Study:

  • To introduce a framework for characterizing melody complexity using multiscale entropy.
  • To measure the complexity of popular songs.
  • To identify complexity levels that explain listener preferences.

Main Methods:

  • Developed a framework based on multiscale entropy.
  • Analyzed a database of popular songs.
  • Correlated melody complexity with song popularity rankings.

Main Results:

  • A framework for quantifying melody complexity was established.
  • A positive association between melody complexity and song popularity was observed.
  • The association between complexity and popularity was not statistically significant after multiple testing adjustments.

Conclusions:

  • Multiscale entropy offers a robust method for assessing melody complexity.
  • Listener preference for song complexity is complex and not solely determined by simple metrics.
  • Further research is needed to understand the nuanced relationship between musical complexity and popularity.