Skip to main content

DT2470 Music Informatics 7.5 credits

Music is everywhere, but how might it be made as searchable as text? This course digs into the details of extracting information from music data. Such technology can be used to search and retrieve music (like Shazam), recommend music and create playlists (like Spotify), and identifying musical instruments, rhythm, chords, and so on. Come see how signal processing can combine with machine learning to create useful and fun applications.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

Headings with content from the Course syllabus DT2470 (Autumn 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

Overview of music informatics, its history and applications as well as a review of basic principles, such as music representation, analog to digital conversion and Fourier transform.
Feature extraction that shows how music data can be described in different domains e.g. time, frequency and time-frequency.
How music content at different levels of abstraction can be expressed and compared with distinctive features.
Ways to model music data by means of statistical machine learning methods.
Evaluation of models of music data and their application in reality.

Intended learning outcomes

After passing the course, the student shall be able to

  • account for how feature extraction works and explain why it is needed
  • recommend methods for comparing and modelling of music data
  • design, implement and evaluate own methods for modelling of music data

in order to

  • be able to describe how information at different levels of abstraction can be extracted from music data (acoustic as well as symbolic) and be used in many applications (e.g., search, retrieval, synthesis)
  • be able to design algorithms for handling and modelling of music data as well as evaluate their performance.

Course disposition

No information inserted

Literature and preparations

Specific prerequisites

  • Completed course DT1130 Spectral transforms 7.5 credits or EQ1220 Signal theory 7.5 credits or equivalent course.
  • Completed course DD2421 Machine learning 7.5 credits or EQ1220 Signal theory 7.5 credits or equivalent course.

Recommended prerequisites

No information inserted


No information inserted


No information inserted

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

A, B, C, D, E, FX, F


  • LAB2 - Laboratory work, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO2 - Project assignment, 3.0 credits, grading scale: A, B, C, D, E, FX, F
  • UPP2 - Written report, 1.0 credits, grading scale: A, B, C, D, E, FX, F
  • ÖVN2 - Exercises, 0.5 credits, grading scale: A, B, C, D, E, FX, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted


Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DT2470

Offered by

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

No information inserted

Transitional regulations

The earlier examination LAB1 is replaced by LAB2, PRO1 is replaced by PRO2, and UPP1 is replaced by UPP2 with ÖVN2.

Supplementary information

In this course, the EECS code of honor applies, see: