MUSS1112 Music Information Retrieval (5 cr)
Study level:
Advanced studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Department of Music, Art and Culture Studies
Curriculum periods:
2017-2018, 2018-2019, 2019-2020
Description
Content
The course provides an overview of main areas and methodologies in information retrieval that are relevant to music research, including machine learning, signal processing, neuroimaging and semantic computing.
Prerequisite: While past experiences with MATLAB or other kinds of programming are considered an asset, this course assumes no prior computer programming experience.
Completion methods
Lectures, Demonstration Workshops, Group work, Survey presentation (individual work), Research project (group work), Project report. More detailed information will be given in syllabus.
Assessment details
Active participation (at least 80%) in contact teaching, passed presentations and project work.
Evaluation criteria will be given in the beginning of the course.
Learning outcomes
Students acquire knowledge regarding the core issues of the discipline, and retain a general guide map for future studies. Basic programming skills using MATLAB environment.
Study materials
• Alluri, V., Toiviainen, P., Jääskeläinen, I. P., Glerean, E., Sams, M., & Brattico, E. (2012). Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. NeuroImage, 59(4), 3677 – 3689.
• Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10):78–87.
• Foote, J. (2000). Automatic audio segmentation using a measure of audio novelty. In IEEE International Conference on Multimedia and Expo, volume 1, pages 452–455. IEEE.
• Hyvärinen, A., and Erkki O. (2000). Independent component analysis: algorithms and applications. Neural networks 13(4), 411-430.
• Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10):78–87.
• Foote, J. (2000). Automatic audio segmentation using a measure of audio novelty. In IEEE International Conference on Multimedia and Expo, volume 1, pages 452–455. IEEE.
• Hyvärinen, A., and Erkki O. (2000). Independent component analysis: algorithms and applications. Neural networks 13(4), 411-430.
Literature
- Domingos, P. (2012). A few useful things to know about machine learning. Communications of the ACM, 55(10):78–87.
- Foote, J. (2000). Automatic audio segmentation using a measure of audio novelty. In IEEE
- Alluri, V., Toiviainen, P., Jääskeläinen, I. P., Glerean, E., Sams, M., & Brattico, E. (2012). Large-scale brain networks emerge from dynamic processing of musical timbre, key and rhythm. NeuroImage, 59(4), 3677 – 3689.
- Hyvärinen, A., and Erkki O. (2000). Independent component analysis: algorithms and applications. Neural networks 13(4), 411-430.
Completion methods
Method 1
Select all marked parts
Parts of the completion methods
x
Teaching (5 cr)
Type:
Participation in teaching
Grading scale:
0-5
Language:
English