musicinformationretrieval.com is a collection of instructional materials for music information retrieval. These materials contain a mix of casual conversation, technical discussion, and Python code.
These pages, including the one you're reading, are authored using Jupyter notebooks, formerly known as IPython notebooks. They are statically hosted using GitHub Pages. The GitHub repository is found here: stevetjoa/stanford-mir.
This material is used during the annual Summer Workshop on Music Information Retrieval at CCRMA, Stanford University. Click here for workshop description and registration.
This site is maintained by Steve Tjoa. For questions, please email steve@stevetjoa.com. Do you have any feedback? Did you find errors or typos? Are you a teacher or researcher and would like to collaborate? Please let me know.
The book Fundamentals of Music Processing (FMP) is a textbook by Meinard Müller, a well-known and prolific researcher in MIR, and published by Springer in 2015.
For many years, the field of music information retrieval has suffered from a lack of consolidated texts which unify its many disparate topics. Until now, students and researchers have had to learn about MIR mostly by reading the original research papers -- a daunting task. FMP is perhaps the best attempt to address this issue. It is a self-contained textbook which covers many MIR tasks such as feature extraction, audio-to-score alignment, musical form and structure, chord recognition, beat tracking, fingerprinting, and source separation.
We are integrating content from FMP into musicinformationretrieval.com in 2016.
The MIR workshop teaches the underlying ideas, approaches, technologies, and practical design of intelligent audio systems using MIR algorithms. It lasts five full days, Monday through Friday. It was founded by Jay LeBoeuf (Real Industry, CCRMA consulting professor) in 2008.
The workshop is intended for students, researchers, and industry audio engineers who are unfamiliar with the field of music information retrieval (MIR). We demonstrate the technologies enabled by signal processing and machine learning. Lectures cover topics such as low-level feature extraction, higher-level features such as chord estimations, audio similarity clustering, search and retrieval, and design and evaluation of classification systems. Our goal is to make these interdisciplinary technologies and complex algorithms approachable.
Knowledge of basic digital audio principles is recommended. Experience with a scripting language such as Python or Matlab is desired. Students are encouraged to bring their own audio source material for course labs and demonstrations.
The workshop consists of half-day lectures, half-day supervised lab sessions, demonstrations, and discussions. Labs allow students to design basic "intelligent audio systems" leveraging existing MIR toolboxes, programming environments, and applications. Labs include creation and evaluation of basic instrument recognition, transcription, and real-time audio analysis systems.
Links redirect to that year's wiki page.