music-computing/amads
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisMusic Information RetrievalPolynomial RegressionScientific Computing
David Whyatt
PROFILE
David Whyatt
Developed a melodic contour analysis feature for the music-computing/amads repository, introducing the PolynomialContour class to enhance music information retrieval workflows. This work focused on centering onset times in musical scores, fitting polynomials using least squares, and selecting the optimal model through Bayesian Information Criterion, all implemented in Python. By leveraging skills in data analysis, scientific computing, and polynomial regression, the feature enables more accurate extraction and analysis of melodic patterns. The approach established a robust foundation for downstream melody analysis and feature extraction, supporting future research and development in computational musicology and automated pattern discovery within musical datasets.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
289
Activity Months1
Your Network
7 peopleShared Repositories
7
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — Added a new melodic contour analysis capability to music-computing/amads via the PolynomialContour class. The feature centers onset times, fits polynomials with least squares, and selects the optimal model using Bayesian Information Criterion (BIC) to analyze melodic contours in scores. This work establishes a robust foundation for downstream melody analysis and feature extraction, enabling more accurate musical pattern discovery. Commit referenced: Polynomial Contour (#84).
1 Commits • 1 Features
Apr 1, 2025April 2025 — Added a new melodic contour analysis capability to music-computing/amads via the PolynomialContour class. The feature centers onset times, fits polynomials with least squares, and selects the optimal model using Bayesian Information Criterion (BIC) to analyze melodic contours in scores. This work establishes a robust foundation for downstream melody analysis and feature extraction, enabling more accurate musical pattern discovery. Commit referenced: Polynomial Contour (#84).
April 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture90.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisMusic Information RetrievalPolynomial RegressionScientific Computing
Repositories Contributed To
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