Music Opinion Dynamics

Code for estimating country-level musical opinion signals and comparing multidimensional DeGroot, Friedkin--Johnsen (FJ), and state-dependent DeGroot (SD-DeGroot) models.

This repository intentionally contains source code only. Datasets, model weights, embeddings, intermediate files, figures, and experiment outputs are excluded.

Current experiment

• Period: 1875--1940
• Agents: Russia, France, Germany, Austria, and Others
• Representation: ChordBERT work embeddings
• State space: PC1--PC23 (more than 90% cumulative explained variance)
• Signal extraction: causal Kalman filtering
• Training transitions: 1875--1925
• Recursive forecasts: 1930, 1935, and 1940
• Models:
  • multidimensional DeGroot;
  • multidimensional FJ;
  • scale-normalized SD-DeGroot.

The PCA-weighted specifications use normalized explained-variance ratios in the estimation loss. Weighting does not alter the transition equation or introduce cross-component interactions.

Repository layout

src/                         ChordBERT, PCA, Kalman, and Python dynamics code
opinion_dynamics/
  degroot_fj/                Variance-weighted DeGroot and FJ
  fj/                        Independent multidimensional FJ implementation
  sd_degroot/                Scale-normalized SD-DeGroot and figure generators
  reproducibility/           Wolfram/Node experiment runners
mathematica/                 Original reference notebooks
chord_embedding/chordbert/   Chord parsing and model inference utilities
midi_experiment/src/         Optional MIDI/BiLSTM supplement

Data and model setup

The main data source is the Cross-era Dataset. Composition years can be obtained from cross-era-classical-work-years.

Large assets are not versioned. Place or generate them under the paths expected by the scripts:

data/                         Raw and processed datasets
chord_embedding/dataset/      Generated chord-sequence splits
chord_embedding/chordbert/    Downloaded or trained model directories
output/                       Generated embeddings, signals, metrics, and figures

All these paths are ignored by Git.

Installation

Python 3.10 or newer is recommended.

python -m venv .venv
pip install -r requirements.txt

The opinion-dynamics scripts additionally require Wolfram Language 13.3 or newer. Figure SVG generators use Node.js 18 or newer and only built-in modules.

Main Python pipeline

python src/preprocess.py
python src/build_chordbert_datasets.py
python src/evaluate_chordbert.py
python src/analyze_chordbert_pca_1875_1940.py
python src/evaluate_degroot_chordbert_window_minmax.py
python src/evaluate_fj_chordbert_window_minmax.py

Scripts resolve paths relative to the repository root and create output/ when needed.

Current opinion-dynamics models

Run from the repository root:

wolframscript -file opinion_dynamics/degroot_fj/VarianceWeighted_DeGroot_FJ_PC1_PC23.wls
wolframscript -file opinion_dynamics/sd_degroot/ScaleNormalized_SDDeGroot_Equal_and_PCAWeighted_PC1_PC23.wls
wolframscript -file opinion_dynamics/sd_degroot/Export_ActualState_W_Diagnostics.wls

The scale-normalized SD-DeGroot model uses

U_ij(t) = log(B_j) - h_i d_ij(t)/tau_d + Gamma_j q_j(t)/tau_q,

where B is a positive simplex, d_ij is RMS stylistic distance, q_j is RMS distance from the cross-country centroid, and tau_d and tau_q are training-period medians. It uses no min--max scaling, clipping, or held-out information.

Reproducibility and leakage control

• Kalman filtering is causal.
• Training uses states through 1925 only.
• Forecasts for 1930--1940 are recursive.
• PCA variance weights affect estimation losses only.
• Actual-state influence matrices for 1930 and 1935 are diagnostic and are never used to produce forecasts or evaluation metrics.

Optional MIDI supplement

See midi_experiment/README.md. Its external dataset and model weights are intentionally excluded.

Citation

@misc{he2026modelingstylisticcoevolutionsymbolic,
      title={Modeling Stylistic Co-evolution in Symbolic Music Heritage Collections}, 
      author={Yulong He and Ivan Smirnov and Yanming Li},
      year={2026},
      eprint={2607.23957},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2607.23957}, 
}