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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.
After passing the course, the student should be able to
• explain how music can be represented in reality and in the computer,
• account for how feature extraction works and explain why it is needed,
• summarise and explain which distinctive features that can be extracted from a music signal, based on time, frequency and time-frequency,
• use existing software libraries for feature extraction and interpret distinctive features that have been extracted from a music signal,
• recommend methods for comparing and modelling of music data,
• design and implement own methods for modelling of music data,
• evaluate a given method for modelling of music data and explain its limitations, in order to
• be able to describe how information on different abstraction levels 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,
• be able to appreciate the latest technology in music informatics and build on it.
• 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.
No information inserted
Computer room or own computer.
Information about the course literature will be announced in the course memo.
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.
The examiner decides, in consultation with KTH's coordinator for disabilities (Funka), about possible adapted examination for students with documented, permanent disabilities. The examiner may permit other examination format for re-examination of individual students.