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
During April 2025, Daniel Whyatt developed a melodic contour analysis feature for the music-computing/amads repository, introducing the PolynomialContour class to support advanced melody analysis. He implemented onset time centering and polynomial regression using least squares, integrating Bayesian Information Criterion for model selection to optimize contour fitting. This approach enables more accurate extraction and analysis of melodic patterns from musical scores, providing a robust foundation for downstream music information retrieval tasks. Daniel utilized Python and scientific computing techniques, applying his skills in data analysis and polynomial regression to deliver a well-structured, extensible solution that addresses core challenges in computational musicology.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
289
Activity Months1
Your Network
6 peopleWork 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
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