Dates (Lecture) (16:15–18:00, Room 3R4.04):
Mo 14.10.2019, Mo 21.10.2019, Mo 28.10.2019, Mo 11.11.2019, Mo 18.11.2019,
Mo 25.11.2019, Mo 02.12.2019, Mo 09.12.2019, Mo 16.12.2019,
Mo 13.01.2020, Mo 20.01.2020, Mo 27.01.2020, Mo 03.02.2020
Examination Dates (Room 3R4.03): To be announced
The lecture has the following format:
Every meeting consists of 90 minutes.
There will be additional exercises for computer science students (see below for details).
Music signals possess specific acoustic and structural characteristics that are not shared by spoken language or audio signals from other domains. In fact, many music analysis tasks only become feasible by exploiting suitable music-specific assumptions. In this course, we study feature design principles that have been applied to music signals to account for the music-specific aspects. In particular, we discuss various musically expressive feature representations that refer to musical dimensions such as harmony, rhythm, timbre, or melody. Furthermore, we highlight the practical and musical relevance of these feature representations in the context of current music analysis and retrieval tasks. Here, our general goal is to show how the development of music-specific signal processing techniques is of fundamental importance for tackling otherwise infeasible music analysis problems.
The lecture closely follows the textbook Fundamentals of Music Processing (FMP). Additionally, the FMP Notebooks offer a collection of educational material, providing detailed textbook-like explanations of central techniques and algorithms in combination with Python code examples that illustrate how to implement the theory.
The following video gives a brief impression about this course.
In this course, we discuss a number of current research problems in music processing or music information retrieval (MIR) covering aspects from information science and digital signal processing. We provide the necessary background information and give numerous motivating examples so that no specialized knowledge is required. However, the students should have a solid mathematical background. The lecture is accompanied by readings from the textbook Fundamentals of Music Processing (FMP) and the research literature. Furthermore, the students are required to experiment with the presented algorithms using Python and/or MATLAB.
Comprehensive framework based on Jupyter notebooks
for teaching and learning fundamentals of music processing.
Exercises (for Computer Science Students)
The exercises, which are particularly provided for computer science students, accompany and extend
the lecture Music Processing Analysis. In the exercise meetings, we review the lecture,
discuss homework problems, deal with programming issues, and realize mini projects that
implement basic algorithms and procedures. If you have any questions regarding the exercise,
please contact Frank Zalkow.