Resources / MIR Tutorials

The following is a list of tutorials presented at previous ISMIR conferences.

2021	2020	2019	2018	2017	2016
2015	2014	2013	2012	2011	2010
2009	2008	2007	2006	2005	2004
2003	2002

2021

• Tempo, Beat, and Downbeat Estimation - Matthew E. P. Davies, Sebastian Böck and Magdalena Fuentes
• Designing Generative Models for Interactive Co-creation - Anna Huang, Jon Gillick and Chris Donahue
• Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers - Johanna Devaney, David R. W. Sears and Daniel Shanahan
• Music Classification: Beyond Supervised Learning, Towards Real-world Applications - Minz Won, Janne Spijkervet and Keunwoo Choi
• Programming MIR Baselines from Scratch: Three Case Studies - Rachel Bittner, Mark Cartwright and Ethan Manilow
• Teaching Music Information Retrieval - George Tzanetakis

2020

• Prototyping and Scaling Audio Research with Klio - Fallon Chen, Lynn Root, Dan Simon and Shireen Kheradpey
• Analysis of Expressive Timing in Recorded Music Performances - Nico Schüler
• Metric Learning for Music Information Retrieval - Brian McFee, Jongpil Lee and Juhan Nam
• Open-Source Tools & Data for Music Source Separation: A Practical Guide for the MIR Practitioner - Ethan Manilow, Prem Seetharaman and Justin Salamon
• Version Identification in the 20s - Furkan Yesiler, Christopher Tralie and Joan Serrà

2019

• Fundamentals of Music Processing: An Introduction using Python and Jupyter Notebooks - Meinard Müller and Frank Zalkow
• Generating Music with GANs: An Overview and Case Studies - Hao-Wen Dong and Yi-Hsuan Yang
• Audiovisual Music Processing - Zhiyao Duan, Slim Essid, Bochen Li and Sanjeel Parekh
• Computational Modeling of Musical Expression: Perspectives, Datasets, Analysis and Generation - Carlos Cancino-Chaćon, Katerina Kosta and Maarten Grachten
• Waveform-based music processing with deep learning - Sander Dieleman, Jordi Pons and Jongpil Lee
• Fairness, Accountability and Transparency in Music Information Research (FAT-MIR) - Emilia Gómez, Andre Holzapfel, Marius Miron and Bob L. Sturm

2018

• Open Source and Reproducible MIR Research - Brian McFee, Thor Kell
• Computational Approaches for Analysis of Non-Western Music Traditions - Xavier Serra, Martin Clayton, Barış Bozkurt
• Statistical Analysis of Results in Music Information Retrieval: Why and How - Julian Urbano, Arthur Flexer
• Music Separation with DNNs: Making It Work- Antoine Liutkus, Fabian-Robert Stöter
• Deep Learning for MIR - Alexander Schindler, Thomas Lidy, Sebastian Böck
• Fundamental Frequency Estimation in Music - Rachel Bittner, Alain de Chevigne, Johanna Devaney
• Optical Music Recognition for Dummies - Jorge Calvo-Zaragoza, Jan Hajič jr., Alexander Pacha, Ichiro Fujinaga
• Overview and New Challenges of Music Recommendation Research in 2018 - Markus Schedl, Peter Knees, Fabien Gouyon

2017

• Bayes and Markov Listen to Music - George Tzanetakis
• Leveraging MIDI files for Music Information Retrieval, video - Colin Raffel
• A basic introduction to audio-related music information retrieval, additional material - Meinard Müller, Christof Weiß
• So you want to conduct a user study in MIR? - Andrew Demetriou, Audrey Laplante, Sally Jo Cunningham, Cynthia Liem
• Machine-Learning for Symbolic Music Generation - Pierre Roy, Jean-Pierre Briot

2016

• Jazz solo analysis between music information retrieval, music psychology, and jazz research - Jakob Abeßer, Klaus Frieler, Wolf-Georg Zaddach
• Music Information Retrieval: Overview, Recent Developments and Future Challenges - Emilia Gómez, Markus Schedl, Xavier Serra, Xiao Hu
• Why is studio production interesting? - Emmanuel Deruty, François Pachet
• Introduction to EEG Decoding for Music Information Retrieval Research - Sebastian Stober, Blair Kaneshiro
• Natural Language Processing for MIR - Sergio Oramas, Luis Espinosa­-Anke, Shuo Zhang, Horacio Saggion
• Why Hip-Hop is interesting - Jan Van Balen, Ethan Hein, Dan Brown

2015

• Why singing is interesting - Simon Dixon, Masataka Goto, Matthias Mauch
• Addressing the music information needs of musicologists - Richard J. Lewis, Ben Fields, Tim Crawford, Kevin Page, David Weigl, Meinard Müller, David Lewis, Christophe Rhodes, Justin Gagen
• Markov logic networks for music analysis - Helene Papadopoulos
• Computation and Flamenco: Why flamenco is interesting for MIR research? - Emilia Gomez, Nadine Kroher, Jose Miguel Díaz-Báñez, Sergio Oramas, Joaquín Mora, Francisco Gómez-Martín
• Using correlation analysis and big data to identify and predict musical behaviors - Jeff C. Smith
• Automatic music transcription - Zhiyao Duan, Emmanouil Benetos

2014

• Why is Greek music interesting? Towards an ethics of MIR - Andre Holzapfel, George Tzanetakis
• Musical structure analysis [Part 1, Part 2, Part 3]- Meinard Müller, Jordan Smith
• Jingju music: concepts and computational tools for its analysis - Rafael Caro Repetto, Ajay Srinivasamurthy, Sankalp Gulati, Xavier Serra
• MiningSuite, a comprehensive framework for music analysis, articulating audio (MIRtoolbox 2.0) and symbolic approaches - Olivier Lartillot

2013

• Why is Brazilian guitar interesting? - François Pachet, Giordano Cabra
• Music autotagging - Mohamed Sordo, Emanuele Coviello
• Deep learning in MIR - Demystifying the dark art: Part 1, Part 2, Part 3 - Philippe Hamel, Eric J. Humphrey, Erik M. Schmidt
• Conditional random fields with application to music analysis - Slim Essid

2012

• Leveraging Repetition to Parse the Auditory Scene - Josh McDermott, Bryan Pardo, and Zafar Rafii
• Music Affect Recognition: The State-of-the-art and Lessons Learned -Xiao Hu, Yi-Hsuan (Eric) Yang
• Reusable software and reproducibility in music informatics research -Mark D Plumbley, Simon Dixon, Chris Cannam
• Why is Jazz Interesting? - François Pachet

2011

• Million Song Dataset - Thierry Bertin-Mahieux, Matthew D. Hoffman, Daniel P.W. Ellis
• Audio Content-based Music Retrieval - Meinard Müller, Joan Serra
• Practical Linked Data for MIR Researchers - David De Roure, Kevin Page
• Musicology - Anja Volk, Frans Wiering

2010

• Music Source Separation and its Applications to MIR - Emmanuel Vincent, Nobutaka Ono
• A Tutorial on Pattern Discovery and Search Methods in Symbolic Music Information Retrieval. - Ian Knopke, Eric Nichols
• A Music-oriented Approach To Music Signal Processing. - Meinard Müller, Anssi Klapuri
• Finding A Path Through The Jukebox – The Playlist Tutorial. - Ben Fields, Paul Lamere

2009

• MIR at the Scale of the Web - Malcolm Slaney, Michael Casey
• Mining the Social Web for Music-Related Data: A Hands-on Tutorial - Claudio Baccigalupo, Ben Fields
• Using Visualizations for Music Discovery - Justin Donaldson, Paul Lamere
• Share and Share Alike, You Can Say Anything about Music in the Web of Data - Share and Share Alike, You Can Say Anything about Music in the Web of Data

2008

• Music Information Retrieval in ChucK; Real-Time Prototyping for MIR Systems & Performance - Ge Wang, Rebecca Fiebrink, Perry R. CookMalcolm Slaney, Michael Casey
• Computational Temporal Aesthetics; Relation Between Surprise, Salience and Aesthetics in Music and Audio Signals - Shlomo Dubnov
• Social Tags and Music Information Retrieval - Paul Lamere, Elias Pampalk
• Survey of Symbolic Data for Music Applications - Eleanor Selfridge-Field, Craig Sapp

2007

• Synchronization and Matching Techiques for Music Data - Meinard Müller, Roger Dannenberg
• Introduction to MIR Toolbox - Olivier Lartillot
• Music Recommendation - Òscar Celma, Paul Lamere
• Techniques for Implementing the Generative Theory of Tonal Music - Keiji Hirata, Satoshi Tojo, Masatoshi Hamanaka

2006

• Computational Rhythm Description - Fabien Gouyon, Simon Dixon
• User Interfaces for Music Information Retrieval - David Gerhard
• MIR for audio signals using Marsyas-0.2 - George Tzanetakis, Luis Gustavo Martins
• Bayesian Methods for Music Signal Analysis - A. Taylan Cemgil

2005

• The combination of musicology, engineering, and interpretation in Beethoven’s 5th symphony - Dalia Atlas
• Looking Beyond the Notes: An Engineering Approach to the Study of Musical Performance - Elaine Chew
• Music Similarity - Elias Pampalk
• Visualization Methods for MIR - Stephan Baumann

2004

• Machine Learning for Music Information Retrieval - Sally Jo Cunningham, David BainbridgeSally Jo Cunningham, David Bainbridge
• Software Frameworks for Analysis of Audio and Music Signals - Xavier Amatriain, George Tzanetakis
• Musical Knowledge, Computational Models and Retrieval - Elaine Chew

2003

• A Survey Tutorial - Joe Futrelle and Stephen Downie
• Music Information Retrieval for Audio Signals - George Tzanetakis
• Music Retrieval and Analysis - Arbee Chen
• Sound, Symbol, and Idea: Three Approaches to Music Query - Eleanor Selfridge-Field, Craig Stuart Sapp

2002

• Digital Music Representations - Geoffroy Peeters, Gérard Assayag
• Music Information Retrieval for Audio Signals - George Tzanetakis
• Modern Methods for Statistical Audio Signal Processing and Characterization - Shlomo Dubnov