The graded evidence of achievements (benoteter Schein) can be picked up in my office (Room 3R4.03) from February 01 on.
The lecture has the following format:
For further information, please contact Prof. Dr. Meinard Müller.
The oral examinations (30 minutes per student) will take place in Am Wolfsmantel 33, Room 3R4.03 (office Prof. Meinard Müller). The examination dates will be announced soon.
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. Other aspects on music processing are covered in the following lectures:
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.