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Perceptual basis of evolving Western musical styles
Pablo H Rodriguez Zivic1, Favio Shifres, Guillermo A Cecchi
1Computer Science Department, University of Buenos Aires, 1428 Buenos Aires, Argentina. prodriguez@dc.uba.ar
Summary
This study reveals data-driven principles of melodic expectation in Western music. Machine learning analysis of historical music data identifies key factors shaping stylistic transitions across musical periods.
Area of Science:
- Cognitive Science
- Musicology
- Computational Linguistics
Background:
- The brain utilizes temporal statistics, known as expectancies, for prediction and object categorization.
- Music perception heavily relies on expectancies across various features and timescales, particularly concerning melodic intervals.
Purpose of the Study:
- To analyze a large Western music dataset using cognitive theories and machine learning.
- To identify historical trends and stylistic transitions in music from Baroque to Post-Romantic periods.
- To propose data-driven principles of melodic expectation.
Main Methods:
- Utilized a large dataset of Western music, condensed as melodic interval counts.
- Applied machine learning techniques informed by cognitive theories of music expectation.
- Analyzed historical trends and stylistic transitions across distinct musical eras.
Main Results:
- Recovered a set of factors that accurately identify historical trends in music.
- Successfully mapped stylistic transitions between Baroque, Classical, Romantic, and Post-Romantic periods.
- Provided musicological and cognitive interpretations for the identified factors.
Conclusions:
- The identified factors represent data-driven principles of melodic expectation.
- This approach offers new insights into the cognitive and musicological underpinnings of musical style.
- Computational analysis can uncover fundamental principles of music perception and history.
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