Latent evolutionary signatures: a general framework for analysing music and cultural evolution
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel framework to identify evolutionary patterns in music, akin to genomics. The model extracts "evolutionary signatures" from musical pieces, improving genre and period prediction.
Area Of Science
- Computational Musicology
- Evolutionary Computation
- Digital Humanities
Background
- Cultural evolution shares similarities with biological evolution.
- Identifying underlying units and evolutionary processes in non-biological systems, particularly music, remains challenging.
- Existing methods lack a framework to jointly analyze musical units and their evolutionary trajectories.
Purpose Of The Study
- To introduce a general framework for identifying underlying units and evolutionary processes in music.
- To model musical styles and organizational principles (harmony, form) as evolutionary processes.
- To extract latent evolutionary signatures from musical corpora for analysis.
Main Methods
- Developed a deep generative architecture, a variational autoencoder with an evolutionary prior.
- Constrained the latent space using an energy-based prior to link songs in evolutionary space.
- Analyzed songs from the McGill Billboard dataset to extract evolutionary signatures.
Main Results
- Identified frequent chord transitions and formal repetition schemes in musical pieces.
- Extracted latent evolutionary signatures linked to harmonic and formal features.
- Demonstrated that evolutionary representations outperform non-evolutionary ones in period and genre prediction tasks.
Conclusions
- The proposed framework successfully models musical evolution and identifies latent evolutionary signatures.
- These signatures provide meaningful embeddings for musical pieces, enhancing analytical tasks.
- This approach offers a new perspective on understanding cultural evolution in music through computational methods.
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