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 advance music information retrieval. He implemented onset time centering and polynomial regression using least squares, enabling the system to model melodic contours in musical scores. To ensure optimal model selection, Daniel incorporated the Bayesian Information Criterion, providing a data-driven approach to feature extraction. Built in Python and leveraging scientific computing techniques, his work established a robust foundation for downstream melody analysis and pattern discovery. This contribution demonstrated depth in data analysis and algorithmic design, addressing core challenges in musical feature modeling.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
289
Activity Months1
Your Network
5 people
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
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