Dates (Lecture) (16:15 - 17:50, Room 3R4.04):
Mo 17.10.2016, Mo 24.10.2016, Mo 31.10.2016, Mo 07.11.2016, Mo 14.11.2016,
Mo 21.11.2016, Mo 28.11.2016, Mo 05.12.2016, Mo 12.12.2016, Mo 19.12.2016,
Mo 09.01.2016, Mo 16.01.2017, Mo 23.01.2017, Mo 30.01.2017
Examination Dates (Room 3R4.03)
Mo 06.02.2017, Tu 07.02.2017, Mo 13.02.2017
The lecture has the following format:
Every meeting consists of 90 minutes
There will be additional exercises for computer science students. Details will be announced later.
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 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 textbooks or the research literature. Furthermore, the students are required to experiment with the presented algorithms using MATLAB.
The general area of Music Processing covers a wide range of subfields and tasks such as music anaylsis, music synthesis, music information retrieval, computer music composition, performance analysis, or audio coding not to speak from close connections to other disciplines such as musicology or library sciences. In this course, we present a selection of topics with an emphasis on music analysis and retrieval.
The exercises, which particulary 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 Stefan Balke.
Supplementary Demos in Matlab and Python can be found here.