Python / Jupyter Notebook for Music, Audio and Machine Learning

• CCRMA MIR Workshop (Notes on Music Information Retrieval)
• LibROSA
• Scikit-learn
• PyTorch

Dataset Links

• Audio Content Analysis: Datasets
• ISMIR.net dataset links
• SigSep: Multi-track audio datasets
• Million Song Dataset (MSD)
• MTG - Music Technology Group (UPF) datasets
• ``mirdata'' an open-source Python library that provides tools for working with common MIR datasets

Review papers in Music and Audio Research

• Content-Based Music Information Retrieval: Current Directions and Future Challenges, Casey et al., 2008
• Music Information Retrieval: Recent Developments and Applications, Schedl et al, 2014
• Automatic Music Transcription: An Overview, Benetos et al., 2018
• Musical Source Separation: An Introduction, Cano et al., 2018
• Deep Learning for Audio-Based Music Classification and Tagging: Teaching Computers to Distinguish Rock from Bach, Nam et al., 2018
• Cross-Modal Music Retrieval and Applications: An Overview of Key Methodologies, Müller et al., 2018
• Audiovisual Analysis of Music Performances: Overview of an Emerging Field, Duan et al., 2018
• An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music, Humphrey et al., 2018

Related Course Websites

• GCT634: Musical Applications of Machine Learning (Spring 2025)
• GCT634: Musical Applications of Machine Learning (Spring 2024)
• GCT634: Musical Applications of Machine Learning (Fall 2022)
• GCT634: Musical Applications of Machine Learning (Fall 2021)