MCT4047 – Music and Machine Learning
Course description
Course content
The aim of the course is to develop knowledge of and practical experience with machine learning algorithms applied in music analysis, music information retrieval, interactive music systems, and algorithmic music.
Learning outcome
Learning outcome
Knowledge
Having completed the course the student will:
- know a range of techniques for supervised, unsupervised and reinforcement machine learning.
- have an overview of different feature extraction methods for audio signals and sensor data.
- be familiar with generic and audio-specific techniques for data mining in music databases.
Skills
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 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.
Admission
Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for 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.
Prerequisites
Formal prerequisite knowledge
Completion of the course MCT4000 - Introduction to Music, Communication and Technology or equivalent documented competencies.
Recommended previous knowledge
It is recommended that the student has basic signal processing and/or programming experience, and some experience in music technology or music cognition.
Teaching
The course is taught using a flipped classroom model, with:
- video lectures and reading assignments in preparations to the workshop.
- a two-weeks intensive workshop (30 hours) with compulsory attendance. In order to take the final exam, students must obtain at least 80% attendance. Students will work on a project chosen in collaboration with the course leader. The project consists in the development of a system based on machine learning for music performance, music analysis, music composition, music recommendation, sound synthesis, etc. Systems designed by students will be showcased on the last day of the course.
Examination
Term paper.
The term paper consists in a report describing design and evaluation of the developed machine learning system, as well as a brief technical documentation of the implemented system and the related dataset. The body of the report should not exceed 2000 words. In order to take the final exam, students must obtain at least 80% attendance.
Language of examination
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.
Evaluation
The course is subject to continuous evaluation. At regular intervals we also ask students to participate in a more comprehensive evaluation.