TSA Improvements Progress

PROGRESS in development
Track improvements and updates to the Temperament Similarity Analysis system. See what's being worked on and what's planned.

🎯 TSA Improvements Progress

Tracking implementation of proposed enhancements to the Temperament Similarity Analysis (TSA) system

Based on Proposed_Changes.md

Total Items

20
Improvements

Completed

17
85% Done

In Progress

0
Active Work

Pending

3
Not Started

Progress by Section

2/2 Expand Dataset
3/3 Improve Similar
2/2 Algorithmic Enh
2/2 Historical Inte
1/2 Practical Tunin
2/2 Visualization I
2/2 Statistical Rig
1/2 User Interface
2/2 Documentation &
0/1 Future Research

1. Expand Dataset Coverage

2/2 completed

Include Temperaments Without Full Fifth Data

Completed High Priority
Use inference methods to fill in missing fifths. Use third-based and comma-based reconstruction when fifths are unavailable. Allow partial scoring with confidence weighting.
Integrated data inference into similarity calculation. Added support for third-based, comma-based, and hybrid inference methods. Confidence weighting applied to similarity scores. Tests passing.
✏️ Edit Notes

Expand Beyond 12-Tone Equal Divisions

Completed Medium Priority
Add 19-EDO, 31-EDO, 55-key, and 53-EDO temperaments. Incorporate non-Western systems (Arabic maqam, Indonesian slendro/pelog, Indian shrutis).
Non-12-tone EDO support implemented (19-EDO, 31-EDO, 53-EDO, 55-key). Non-Western systems added: Arabic maqam, Indonesian slendro/pelog, Indian shruti. All tests passing.
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2. Improve Similarity Metrics

3/3 completed

Test Alternative Weightings

Completed High Priority
Test weightings using cross-validation. Learn weights dynamically using clustering or ML. Provide user-selectable weighting profiles: Structural, Harmonic, Tuning-practice, Historical-likelihood.
Weighting profiles implemented and integrated. Users can select from Structural, Harmonic, Balanced, Equal, Fifths-Heavy, Comma-Focused, Tuning-Practice, and Historical-Likelihood profiles. Sensitivity analysis available to test robustness across weightings.
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Add Perceptual Metrics

Completed Medium Priority
Incorporate psychoacoustic principles: interval roughness, harmonic dissonance curves, beat rate modeling using partial structures.
Perceptual metrics implemented: interval roughness (Plomp-Levelt model), harmonic dissonance curves, beat rate modeling using partial structures. All tests passing.
✏️ Edit Notes

Enable Multiple Distance Metrics

Completed Medium Priority
Implement cosine similarity, Manhattan distance, Chebyshev distance, Wasserstein distance (Earth mover's metric on pitch-space distributions).
Multiple distance metrics implemented: cosine similarity, Manhattan distance, Chebyshev distance, Wasserstein distance (Earth movers metric), plus Euclidean and circular variants. All tests passing.
✏️ Edit Notes

3. Algorithmic Enhancements

2/2 completed

Improve Rotation-Invariant Logic

Completed Medium Priority
Classify rotations into meaningful categories (e.g., major-key, minor-key, dominant-key alignments).
Rotation classification implemented: categorizes rotations into major-key, minor-key, dominant-key, subdominant-key, relative major/minor, and structural alignments. Distinguishes musical vs structural similarity. All tests passing.
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Add Robustness Testing

Completed High Priority
Introduce noise simulations (random perturbation ±2 cents), rounding error sensitivity, stability analysis across beat-rate approximations.
➕ Add Notes

4. Historical Integration Layer

2/2 completed

Metadata-Enriched Temperaments

Completed Medium Priority
Include date, geographic region, composer associations, surviving tuning instructions, known theoretical lineage (Zarlino → Vallotti → Young).
Metadata-enriched temperaments implemented: structured metadata interface for date, geographic region, composer associations, tuning instructions, theoretical lineage. Helper functions for lineage and school checking. All tests passing.
✏️ Edit Notes

Historical Likelihood Engine

Completed Low Priority
Estimate probability that a temperament belongs to a historical school. Detect likely misattributions.
Historical likelihood engine implemented: estimates probability that a temperament belongs to a historical school based on structural similarity, temporal/geographic consistency, and lineage. Misattribution detection included. All tests passing.
✏️ Edit Notes

5. Practical Tuning Integration

1/2 completed

Audio-Based Comparison

Pending Low Priority
Support upload of audio files, FFT-based measurement of actual tuned instruments, automatic temperament reconstruction from recorded piano samples.
➕ Add Notes

Technician Workflow Tools

Completed Medium Priority
Add aural test suggestions based on similarity output, recommended 'closest temperament alternatives', visual beat-rate charts for practical tuning.
Technician workflow tools implemented: aural test suggestions based on similarity, recommended closest temperament alternatives, beat rate charts for practical tuning. Complete workflow summary generator. All tests passing.
✏️ Edit Notes

6. Visualization Improvements

2/2 completed

Similarity Map (2D or 3D)

Completed Medium Priority
Use dimensionality reduction (e.g., UMAP, t-SNE) to show temperament families, historical evolution clusters, outlier structures.
Similarity map framework implemented: PCA and MDS projections for 2D/3D visualization, clustering algorithm, outlier detection, export format for external UMAP/t-SNE libraries. Ready for integration with visualization tools.
✏️ Edit Notes

Comma Pattern Lattices

Completed Low Priority
Graphical representation of comma distributions: Syntonic, Pythagorean, Diaschisma, Greater diesis. Would provide clarity on temperament 'DNA'.
Comma pattern visualization implemented: generates comma pattern data, compares patterns between temperaments, creates lattice structures for graph visualization, exports for external tools. Ready for integration with visualization libraries.
✏️ Edit Notes

7. Statistical Rigor Enhancements

2/2 completed

Null Hypothesis Testing

Completed High Priority
Generate random temperament sets to establish baseline similarity probabilities. How often do random temperaments reach ≥80% similarity? Establish genuine 'significance thresholds'.
➕ Add Notes

Error Bars and Confidence Intervals

Completed High Priority
Present similarity scores with confidence intervals, stability ranges under perturbation. This guards against overinterpretation of fragile similarities.
➕ Add Notes

8. User Interface & Accessibility

1/2 completed

Web-Based Interface

Pending Medium Priority
Upload temperament tables, real-time similarity calculation, interactive rotation slider, historical annotations displayed on hover.
➕ Add Notes

API for Researchers

Completed Low Priority
A REST or Python API allowing batch comparison, dataset ingestion, custom similarity metric creation.
Research API implemented: batch comparison, dataset ingestion with validation, custom similarity metric creation, temperament search by criteria. Programmatic access for researchers. All functionality exported.
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9. Documentation & Transparency

2/2 completed

Stronger Documentation of Assumptions

Completed Medium Priority
Clarify why certain metrics were chosen, what weights mean, limitations of 'structural similarity' vs. 'musical similarity'.
Comprehensive methodology documentation created: explains why metrics were chosen, what weights mean, limitations of structural vs musical similarity, best practices for interpretation. File: docs/METHODOLOGY_ASSUMPTIONS.md
✏️ Edit Notes

Open Datasets

Completed Low Priority
Publish full temperament tables, raw fifth-offset values, intermediate computational states.
Data export functionality implemented: export temperament tables (CSV/JSON), raw fifth-offset values, intermediate computational states, similarity matrices, complete datasets. All tests passing.
✏️ Edit Notes

10. Future Research Directions

0/1 completed

Future Research Directions

Pending Low Priority
Explore ML models that can automatically cluster historical tuning styles. Cross-reference tuning instructions with reconstructed data using text analysis. Develop a 'temperament fingerprinting' model for identifying unknown or hybrid tunings. Integrate temperament with performance practice studies (e.g., Bach, Rameau, Couperin).
➕ Add Notes
Last updated: November 30, 2025 9:16 PM