5 ECTS (Lecture with Exercises; only for selected study programmes)
Time (Lecture): Winter Term 2020/2021, Mo 16:15–18:00 (1. Lecture: 02.11.2020, via ZOOM)
Link and access information for our ZOOM meetings can be found at StudOn (see below).
Time (Exercises): Winter Term 2020/2021, Mo 14:15–15:45 (1. Excercise: 09.11.2020, via ZOOM)
Exam (graded): Oral examination at the end of term
Mo 02.11.2020, Mo 09.11.2020, Mo 16.11.2020, Mo 23.11.2020, Mo 30.11.2020,
Mo 07.12.2020, Mo 14.12.2020, Mo 21.12.2020,
Mo 11.01.2021, Mo 18.01.2021, Mo 25.01.2021, Mo 01.02.2021, Mo 08.02.2021
Due to the COVID-19 pandemic, the lecture Music Processing Analysis will be offered as a fully virtual course (via ZOOM).
Rather than following the traditional lecturing format, this course will be inspired by the flipped classroom concept. Being offered in this format for the first time, the lecture will have some experimental character. Important elements are:
In particular, students are required to be prepared prior to the lecture. The lecture time will be used for a short summary, the deepening of the most important aspects, and for having a question–answering dialogue with participants. Note that this concept will require a lot of work and dedication on the side of the lecturer and participants.
As a technical requirement, all participants must have access to a computer capable of running the ZOOM video conferencing software (as provided by FAU), including audio and video transmission as well as screensharing. Furthemore, a regular web browser (preferably Google Chrome) to access the FMP Notebooks and the Python development environment is needed.
To ensure privacy, participants are not permitted to record the ZOOM sessions. Furthermore, ZOOM links may not be distributed. The required material will be made available in the following way:
All FAU students can get an electronic copy of the required textbook via SpringerLink. To this end, you need to be logged in via an FAU account. It may be benefial to have a physical copy of the book to allow for offline reading. You may print out the required pages, or you may purchase a high-quality softcover edition at low cost (MyCopy softcover) following SpringerLink.
The slides used in the lecture are made publically available as PDF.
The FMP Notebooks along with all required Python code and audio examples are publically available.
All required videos are publically available.
Questions and answers during the ZOOM sessions will be collected and made freely accessible.
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 and music information retrieval (MIR) covering aspects from information science and audio signal processing. While we provide the necessary background information, a good understanding of general concepts in signal processing and data science (e.g., algorithms, data structures) as well as strong mathematical background is required. Furthermore, good programming skills are a prerequisite for participating in the exercises. In particular, participants are required to experiment with the presented algorithms using Python and Jupyter notebooks. Specific knowledge in music theory is not required, but basic knowledge and a strong interest in music are extremely helpful to get enthusiastic about the field of music processing.
The lecture material includes textbook passages, notebooks, handouts of slides, videos, and so on. In the following list, you find detailed descriptions and links to the material. If you have any questions regarding the lecture, please contact Prof. Dr. Meinard Müller.
The exercises, which are mainly offered to 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. Note that good programming skills are a prerequisite for participating in the exercises. In particular, we assume basic knowledge in Python as covered by the PCP Notebooks. If you have any questions regarding the exercise,
please contact Michael Krause.