Lecture: Music Processing Analysis, Winter Term 2020/2021
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- Instructor: Prof. Dr. Meinard Müller
- Tutor: Michael Krause
- 2.5 ECTS (Lecture only)
- 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 (only by appointment in lecture)
- Monday, February 8
- Monday, February 22
- Monday, March 22
- Monday, April 12
- Dates (Lecture):
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).
- Participation in the ZOOM session is only possible for FAU students. The ZOOM access information
for this course will be made available via StudOn. Therefore, you must register via StudOn prior to the first lecture. Please contact Michael Krause for questionson StudOn.
- 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.
Accompanying Textbook and Notebooks
Fundamentals of Music Processing
Audio, Analysis, Algorithms, Applications
Comprehensive framework based on Jupyter notebooks
for teaching and learning fundamentals of music processing.
Lecture: Topics, Material, Instructions, Questions & Answers
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.
Exercises: Material, Instructions
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.
Note: You must complete the exercises before coming to the exercise session. So you must have prepared the Exercise on Music Representations by 09.11!
- Exercise: Music Representations
Relevant for the excercise on 09.11.2020
- Exercise: Fourier Analysis of Signals
Relevant for the excercise on 16.11.2020 (up until, including, "Sampling, Aliasing, Beating")
Relevant for the excercise on 23.11.2020 (up until, including, "Frequency Grid")
Notebook has been updated on 18.11.2020
Relevant for the excercise on 07.12.2020 (everything)
Notebook has been updated on 30.11.2020
- Lab Exams on STFT and HPSS
Will take place on 30.11.2020
On 30.11.2020, you will be asked to present your solutions to the two lab notebooks on STFT and HPSS. You may work on this in pairs! Please have your solutions ready to present via screensharing. Do not hesitate to ask questions via mail or in the exercise session on 23.11!
The environment.yml provided in the .zip-archives don't seem to work currently. Use the FMP environment instead!
- Brainstorming session for project work: 07.12.2020 - Bring your ideas for music processing projects you want to tackle in the rest of the semester!
Don't forget to additionally prepare the exercises on the FFT, see above.
- Introductory presentations for project work: 14.12.2020 - Present your goals and outline techniques you want to use. Max 4 minutes per group. Use Jupyter notebooks as your slides.
- Mandatory reading assignment: Due via mail to Michael on Sunday, 17.01.2021 - Each student must summarize a chapter from the FMP textbook. Choose the chapter most related to your project! You must use the provided Latex template (2 pages without references; do not copy text or figures from the book). These are individual submissions - do not work in groups.
- Final presentations on projects incl. demo session: 01.02.2021 - 2 minutes teaser-presentations per group, afterwards demo sessions.