MCT4052 ¨C Music and Machine Learning

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.
• 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.

Admission to the course

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.

Recommended previous knowledge

It is recommended that the students have completed the course MCT4001 ¨C Sound and Music Programming or that students are familiar with Python programming with packages for scientific computing, and have some knowledge in sound and music computing.

Overlapping courses

• 5 credits overlap with MCT4047 ¨C Music and Machine Learning (continued).
• 5 credits overlap with HFIMV9052 ¨C Music and Machine Learning.

Teaching

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

• Video lectures, readings and practical tasks in preparation for the workshops.

• 10 workshops of 4 hours.

Compulsory activities

• Participation in 8 out of 10 workshops

• Eight qualifying assignments including a project demonstration.

Information about assignments and the deadlines are available in Canvas. Students have to hand in the assignments within the given deadline, and are responsible for familiarizing themselves with the requirements for the compulsory activities.

The compulsory activities are only valid within the current semester. All compulsory activities must be approved in order to sit for the exam. Students are responsible to keep track of registered absences and check that everything has been approved.

This is how you apply for a valid absence from compulsory activity/compulsory attendance.

Examination

• Portfolio (semester project)??

  • The project consists in the design and implementation of a music-related machine learning system. This may include systems for music classification, sound recognition, music recommendation, music database mining, algorithmic music composition, sound processing or generation. Projects are decided by the students and must be approved by the course responsible. Produced project material must be submitted with documentation to replicate the results. Projects are summarized in an academic paper describing design, implementation and evaluation of the developed system, with a particular focus on related works, selected machine learning techniques, dataset, evaluation strategy, and consideration of associated musical aspects. The paper should also include critical thoughts and reflections. The body of the paper should be approximately 4000 words.

You have to fulfill the requirements of the compulsory activities to sit the exam.

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.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.