On the Complexity Analysis and Visualization of Musical Information
António M Lopes1, J A Tenreiro Machado2
1UISPA-LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal.
Entropy (Basel, Switzerland)
|December 3, 2020
Summary
This study uses mathematical tools to analyze digital music, revealing patterns that effectively assess musical complexity and correlate with artists' production.
Area of Science:
- Computational musicology
- Digital signal processing
- Data analysis
Background:
- Characterizing music requires robust analytical methods.
- Previous approaches may not fully capture musical complexity.
- Digital music representations offer new analytical possibilities.
Purpose of the Study:
- To apply mathematical and computational tools for music characterization.
- To assess the complexity of musical information in digital music.
- To correlate data patterns with artists' musical production.
Main Methods:
- Utilized complexity, dimensionality-reduction, clustering, and visualization techniques.
- Analyzed digital music representations using distinct indices.
- Employed multidimensional scaling (MDS) for data visualization.
Main Results:
- Identified distinct patterns in the digital music data.
- Demonstrated the effectiveness of the applied computational tools.
- Correlated analytical findings with artists' musical output.
Conclusions:
- The proposed approach is effective for assessing musical complexity.
- Mathematical and computational methods provide valuable insights into music.
- Data-driven analysis can characterize and understand musical information.
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