Skip to content

Research Notebooks

Interactive Jupyter notebooks exploring music technology, MIR, and deep learning

Available Research Notebooks

Research

Arab-Andalusian Motif Development Analysis

Computational analysis of melodic centos (motifs) in Arab-Andalusian music. This notebook explores motif development patterns in traditional Iraqi Ajam repertoire, analyzing the distribution and frequency of melodic patterns across musical scores using music21 and pandas.

Date: Jan 2022
Reading time: 20 min
jupyter python music-analysis
View Notebook

About These Notebooks

These Jupyter notebooks showcase my research and experiments in music technology, providing detailed walkthroughs of algorithms, analyses, and implementations. Each notebook includes executable code, visualizations, and comprehensive documentation.

Topics covered include Music Information Retrieval (MIR), computational musicology, pattern recognition in musical scores, audio feature extraction, and deep learning applications in music technology.

The notebooks are designed to be educational resources for researchers, students, and practitioners interested in computational approaches to music analysis. They demonstrate practical applications of machine learning, signal processing, and music theory.

All code is executable and reproducible. For interactive versions or to run the code yourself, visit the GitHub repository.