MCT4052 – Music and Machine Learning

Schedule, syllabus and examination date

Choose semester

Course content

The aim of the course is to develop knowledge of and practical experience with machine learning algorithms applied to music analysis, music information retrieval, interactive music systems, and algorithmic music.

Learning outcome


Having completed the course, the student will:

  • know about various techniques for supervised, unsupervised and reinforcement machine learning.
  • know different feature extraction methods for sound, music and sensor data.
  • be familiar with generic and audio-specific techniques for data mining in music databases.


Having completed the course, the student will:

  • be able to use machine learning techniques for pragmatic and creative purposes in the broad context of music.
  • be able to carry out content-based search in audio collections using music information retrieval techniques.
  • be able to use techniques for action and gesture recognition in interactive music systems.
  • be able to critically reflect on the use of machine learning techniques in applications within and outside the field of music.


A maximum of 20 students can be admitted to this course due to the capacity of the specialized room.

Students admitted at UiO must apply for courses in Studentweb.

Students enrolled in other Master's Degree Programmes can, on application, be admitted to the course if this is cleared by their own study programme.

If you are not already enrolled as a student at UiO, please see our information about admission requirements and procedures.

If the number of enrolled students exceed the capacity, they will be ranked as follows:

1.   Master students admitted to the Music, Communication and Technology programme.

2.   Master students at the Department of Musicology who have the course approved in their study plan.

3.   Master students at the University of Oslo who have the course approved in their study plan.

4.   Others.


Formal prerequisite knowledge

Completion of the course MCT4001 – Introduction to Music, Communication and Technology or equivalent documented competencies.

Recommended previous knowledge

It is recommended that the student has basic programming experience, and some experience in music technology.

Overlapping courses

5 credits overlap with MCT4047 – Music and Machine Learning (continued)


The course is taught using a flipped classroom model and blended learning methods, and includes:

  • Video lectures, readings and assignment in preparation for the workshops.
  • 10 workshops of 4 hours with compulsory attendance.

In order to qualify for the exam, students must obtain at least 80% attendance and one mandatory project assignment must be approved. Read more about compulsory activities here.


Project assignment (50%)

The project consists in the design and implementation of a music-related machine learning system. Possible application includes music classification, sound recognition, music recommendation, algorithmic music composition, sound processing or generation. Projects are decided by the students and must be approved by the course responsible. Projects are showcased at the end of the semester in a short live demonstration. Associated technical material and documentation must be submitted as well.

Term paper (50%)

The paper consists in a report describing design and evaluation of the developed machine learning system, with a particular focus on the selected machine learning and feature extraction techniques, the evaluation methodology and associated dataset. The paper should also include critical thoughts and reflections with respect to the associated music-related application. The body of the report should not exceed 3500 words.

Submit assignments in Inspera

You submit your assignment in the digital examination system Inspera. Read about how to submit assignments in Inspera.

Use of sources and citation

You should familiarize yourself with the rules that apply to the use of sources and citations. If you violate the rules, you may be suspected of cheating/attempted cheating.

Language of examination

The examination text is given in English, and you submit your response in English.

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.

Explanations and appeals

Resit an examination

Withdrawal from an examination

It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.

Special examination arrangements

Application form, deadline and requirements for special examination arrangements.


The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.

Facts about this course






Every spring


Every spring

Teaching language