Educational Materials

The resources are sorted by decreasing year of publication. If you would like to add a resource please leave a ticket on GitHub.

Article Link
An article that provides a MIR survey. [link]
Book Link
Peter Knees and Markus Schedl. "Music Similarity and Retrieval: An Introduction to Audio- and Web-based Strategies." Springer, 2016 [link]
Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph. "Music Data Analysis: Foundations and Applications." Chapman & Hall/CRC Computer Science & Data Analysis, 2016 [link]
Meinard Müller. "Fundamentals of Music Processing." Springer, 2015. [link]
Alexander Lerch. "An Introduction to Audio Content Analysis." Wiley, 2012. [link]
Òscar Celma. "Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space." Springer, 2010. [link]
Meinard Müller. "Information Retrieval for Music and Motion." Springer, 2007. [link]
William Arthur Sethares. "Rhythm and Transforms." Springer, 2007. [link]
Anssi P. Klapuri and Manuel Davy, editors. "Signal Processing Methods for Music Transcription." Springer, New York, 2006. [link]
William A. Sethares. "Tuning, Timbre, Spectrum, Scale." Springer, London, 1998. [link]
Website Link
Matlab code for feature extraction, pitch tracking, key detection, onset detection, and links to data sets and MIR-related software projects. [link]
A centralized collection of teaching resources related to Music Information Retrieval. It is addressed to teachers and students interested on these technologies from an educational point of view. Current resources include the following: a list of courses related to MIR in different levels, institutions and countries; a collaborative (small) list of teaching materials, such as exercises, musical examples, code; and a list of datasets and reference annotations. [link]
"This tutorial provides a survey of the field of Music Information Retrieval (MIR), that aims, among other things, at automatically extracting semantically meaningful information from various representations of music entities, such as audio, scores, lyrics, web pages or microblogs. The tutorial is designed for students, engineers, researchers, and data scientists who are new to MIR and want to get introduced to the field." [link]