MUSA2004 Music Information Research (5 cr)

Study level:
Intermediate studies
Grading scale:
0-5
Language:
English
Responsible organisation:
Department of Music, Art and Culture Studies
Curriculum periods:
2024-2025, 2025-2026, 2026-2027, 2027-2028

Description

The course provides an overview of main areas and methodologies in computational analysis of music, including signal processing and feature extraction, machine learning, and semantic computing.

Learning outcomes

After completing the course the student will:

· be able to program in computer languages relevant to Music Information Research (e.g. MATLAB)

· understand the processes involved in computational musical feature extraction (e.g. extract from a digital audio signal information related to pitch, timbre, rhythm, meter, tonality)

· understand and implement methods for music classification, segmentation

· understand methods of semantic computing, and/or is able to retrieve large data using music APIs

Additional information

2nd or 3rd year of bachelor studies.

Study materials

Eerola, T. & Toiviainen, P. (2004). MIDI Toolbox: MATLAB Tools for Music Research. University of Jyväskylä: Kopijyvä, Jyväskylä, Finland.

O. Lartillot, MIRtoolbox 1.8.2 User’s Manual,, University of Oslo, Norway RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion www.jyu.fi/music/coe/materials/mirtoolbox

Schedl M, Gómez E, Urbano J. Music information retrieval: recent developments and applications. Foundations and Trends in Information Retrieval. 2014 Sept 12; 8 (2-3): 127-261. DOI: 10.1561/1500000042 https://repositori.upf.edu/handle/10230/27565 

Literature

  • Eerola, T. & Toiviainen, P. (2004). MIDI Toolbox: MATLAB Tools for Music Research. University of Jyväskylä: Kopijyvä, Jyväskylä, Finland.
  • O. Lartillot, MIRtoolbox 1.8.2 User’s Manual,, University of Oslo, Norway RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion
  • Schedl M, Gómez E, Urbano J. Music information retrieval: recent developments and applications. Foundations and Trends in Information Retrieval. 2014 Sept 12; 8 (2-3): 127-261. DOI: 10.1561/1500000042

Completion methods

Method 1

Evaluation criteria:
Attendance; computer programming assignments; small-scale empirical or literature-based research projects; project presentations
Select all marked parts
Parts of the completion methods
x

Participation in teaching (5 cr)

Type:
Participation in teaching
Grading scale:
0-5
Language:
English

Teaching